Alzheimer’s Research & Therapy (Mar 2022)

A multi-regression framework to improve diagnostic ability of optical coherence tomography retinal biomarkers to discriminate mild cognitive impairment and Alzheimer’s disease

  • Jacqueline Chua,
  • Chi Li,
  • Lucius Kang Hua Ho,
  • Damon Wong,
  • Bingyao Tan,
  • Xinwen Yao,
  • Alfred Gan,
  • Florian Schwarzhans,
  • Gerhard Garhöfer,
  • Chelvin C. A. Sng,
  • Saima Hilal,
  • Narayanaswamy Venketasubramanian,
  • Carol Y. Cheung,
  • Georg Fischer,
  • Clemens Vass,
  • Tien Yin Wong,
  • Christopher Li-Hsian Chen,
  • Leopold Schmetterer

DOI
https://doi.org/10.1186/s13195-022-00982-0
Journal volume & issue
Vol. 14, no. 1
pp. 1 – 11

Abstract

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Abstract Background Diagnostic performance of optical coherence tomography (OCT) to detect Alzheimer’s disease (AD) and mild cognitive impairment (MCI) remains limited. We assessed whether compensating the circumpapillary retinal nerve fiber layer (cpRNFL) thickness for multiple demographic and anatomical factors as well as the combination of macular layers improves the detection of MCI and AD. Methods This cross-sectional study of 62 AD (n = 92 eyes), 108 MCI (n = 158 eyes), and 55 cognitively normal control (n = 86 eyes) participants. Macular ganglion cell complex (mGCC) thickness was extracted. Circumpapillary retinal nerve fiber layer (cpRNFL) measurement was compensated for several ocular factors. Thickness measurements and their corresponding areas under the receiver operating characteristic curves (AUCs) were compared between the groups. The main outcome measure was OCT thickness measurements. Results Participants with MCI/AD showed significantly thinner measured and compensated cpRNFL, mGCC, and altered retinal vessel density (p < 0.05). Compensated RNFL outperformed measured RNFL for discrimination of MCI/AD (AUC = 0.74 vs 0.69; p = 0.026). Combining macular and compensated cpRNFL parameters provided the best detection of MCI/AD (AUC = 0.80 vs 0.69; p < 0.001). Conclusions and relevance Accounting for interindividual variations of ocular anatomical features in cpRNFL measurements and incorporating macular information may improve the identification of high-risk individuals with early cognitive impairment.

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