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. 2024 Jan;20(1):316-329.
doi: 10.1002/alz.13442. Epub 2023 Aug 23.

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study

Frank C T van der Heide  1   2   3   4 , Indra L M Steens  1   2 , Betsie Limmen  1   2 , Sara Mokhtar  1   2   3   4 , Martin P J van Boxtel  3   4 , Miranda T Schram  1   2   4   5 , Sebastian Köhler  3   4 , Abraham A Kroon  1   2 , Carla J H van der Kallen  1   2 , Pieter C Dagnelie  1   2 , Martien C J M van Dongen  6   7 , Simone J P M Eussen  1   7 , Tos T J M Berendschot  8 , Carroll A B Webers  8 , Marleen M J van Greevenbroek  1   2 , Annemarie Koster  6   9 , Thomas T van Sloten  1   10 , Jacobus F A Jansen  4   11 , Walter H Backes  4   11 , Coen D A Stehouwer  1   2
Affiliations

Thinner inner retinal layers are associated with lower cognitive performance, lower brain volume, and altered white matter network structure-The Maastricht Study

Frank C T van der Heide et al. Alzheimers Dement. 2024 Jan.

Abstract

Introduction: The retina may provide non-invasive, scalable biomarkers for monitoring cerebral neurodegeneration.

Methods: We used cross-sectional data from The Maastricht study (n = 3436; mean age 59.3 years; 48% men; and 21% with type 2 diabetes [the latter oversampled by design]). We evaluated associations of retinal nerve fiber layer, ganglion cell layer, and inner plexiform layer thicknesses with cognitive performance and magnetic resonance imaging indices (global grey and white matter volume, hippocampal volume, whole brain node degree, global efficiency, clustering coefficient, and local efficiency).

Results: After adjustment, lower thicknesses of most inner retinal layers were significantly associated with worse cognitive performance, lower grey and white matter volume, lower hippocampal volume, and worse brain white matter network structure assessed from lower whole brain node degree, lower global efficiency, higher clustering coefficient, and higher local efficiency.

Discussion: The retina may provide biomarkers that are informative of cerebral neurodegenerative changes in the pathobiology of dementia.

Keywords: brain structural connectivity; brain volume; clustering coefficient; cognitive function; cognitive performance; global efficiency; graph theory; grey matter; local efficiency; magnetic resonance imaging (MRI); optical coherence tomography (OCT); retinal imaging; retinal neurodegeneration; white matter; whole brain node degree.

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Conflict of interest statement

The authors declare no conflicts of interest. Author disclosures are available in the Supporting information.

Figures

FIGURE 1
FIGURE 1
Study population selection. *Not mutually exclusive. HDL, high density lipoprotein; mGCL, macular ganglion cell layer; mIPL, macular inner plexiform layer; MRI, magnetic resonance imaging; mRNFL, macular retinal nerve fiber layer; OCT, optical coherence tomography; pRNFL, peripapillary retinal nerve fiber layer
FIGURE 2
FIGURE 2
Associations of retinal layer thickness indices (per SD lower) with global cognitive performance, total white matter volume, whole brain node degree, and local efficiency. Regression coefficients (β) represent the difference in global cognitive performance, total white matter volume, whole brain node degree, and local efficiency (all expressed in SD) per SD lower pRNFL thickness, mRNFL thickness, mGCL thickness, and mIPL thickness. The associations of retinal layer thicknesses with total grey matter volume, global efficiency and clustering coefficient are not shown as results for these measures were similar to those shown in Figure 2 (the results for total grey matter volume were similar to the results for total white matter volume; the results for global efficiency were directionally similar to the results for whole brain node degree; and the results for clustering coefficient were directionally and numerically similar to the results for local efficiency; all shown in Tables 3 and 4). Values per SD or quartile of retinal thickness are reported in the legend of Table 2; values per SD of MRI measures are reported in the legend of Tables 3 and 4. Variables entered in the models in addition to retinal indices are age, sex, glucose metabolism status, educational level, spherical equivalent, MRI lag time (only applicable for MRI indices), office systolic blood pressure, history of cardiovascular disease, waist circumference, smoking status, alcohol consumption, total/HDL cholesterol ratio, lipid‐modifying medication, and antihypertensive medication. *Indicates statistically significant (P < 0.05). Superscript (1) indicates that the associations of indices of pRNFL and mRNFL with cognitive performance are shown for low versus high thickness instead of per SD lower. CI, confidence interval; HDL, high‐density lipid; mGCL, macular ganglion cell layer; mIPL, macular inner plexiform layer; MRI, magnetic resonance imaging; mRNFL, macular retinal nerve fiber layer; pRNFL, peripapillary retinal nerve fiber layer; SD, standard deviation
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