Similar pattern of atrophy in early- and late-onset Alzheimer's disease

Early molecular events of autosomal-dominant Alzheimer's disease in marmosets with PSEN1 mutations

INTRODUCTION

Fundamental questions remain about the key mechanisms that initiate Alzheimer's disease (AD) and the factors that promote its progression. Here we report the successful generation of the first genetically engineered marmosets that carry knock-in (KI) point mutations in the presenilin 1 (PSEN1) gene that can be studied from birth throughout lifespan.

METHODS

CRISPR/Cas9 was used to generate marmosets with C410Y or A426P point mutations in PSEN1. Founders and their germline offspring are comprehensively studied longitudinally using non-invasive measures including behavior, biomarkers, neuroimaging, and multiomics signatures.

RESULTS

Prior to adulthood, increases in plasma amyloid beta were observed in PSEN1 mutation carriers relative to non-carriers. Analysis of brain revealed alterations in several enzyme-substrate interactions within the gamma secretase complex prior to adulthood.

DISCUSSION

Marmosets carrying KI point mutations in PSEN1 provide the opportunity to study the earliest primate-specific mechanisms that contribute to the molecular and cellular root causes of AD onset and progression.

HIGHLIGHTS

We report the successful generation of genetically engineered marmosets harboring knock-in point mutations in the PSEN1 gene. PSEN1 marmosets and their germline offspring recapitulate the early emergence of AD-related biomarkers. Studies as early in life as possible in PSEN1 marmosets will enable the identification of primate-specific mechanisms that drive disease progression.

The Associations Between Serum Insulin-like Growth Factor-I, Brain White Matter Volumes, and Cognition in Mild Cognitive Impairment and Alzheimer's Disease

Background

Insulin-like growth factor-I (IGF-I) regulates myelin, but little is known whether IGF-I associates with white matter functions in subjective and objective mild cognitive impairment (SCI/MCI) or Alzheimer's disease (AD).

Objective

To explore whether serum IGF-I is associated with magnetic resonance imaging - estimated brain white matter volumes or cognitive functions.

Methods

In a prospective study of SCI/MCI (n = 106) and AD (n = 59), we evaluated the volumes of the total white matter, corpus callosum (CC), and white matter hyperintensities (WMHs) as well as Mini-Mental State Examination (MMSE), Trail Making Test A and B (TMT-A/B), and Stroop tests I-III at baseline, and after 2 years.

Results

IGF-I was comparable in SCI/MCI and AD (113 versus 118 ng/mL, p = 0.44). In SCI/MCI patients, the correlations between higher baseline IGF-I and greater baseline and 2-year volumes of the total white matter and total CC lost statistical significance after adjustment for intracranial volume and other covariates. However, after adjustment for covariates, higher baseline IGF-I correlated with better baseline scores of MMSE and Stroop test II in SCI/MCI and with better baseline results of TMT-B and Stroop test I in AD. IGF-I did not correlate with WMH volumes or changes in any of the variables.

Conclusions

Both in SCI/MCI and AD, higher IGF-I was associated with better attention/executive functions at baseline after adjustment for covariates. Furthermore, the baseline associations between IGF-I and neuropsychological test results in AD may argue against significant IGF-I resistance in the AD brain.

A biomarker-validated time scale in years of disease progression has identified early- and late-onset subgroups in sporadic Alzheimer's disease

BACKGROUND

It is possible to calculate the number of years to the expected clinical onset (YECO) of autosomal-dominant Alzheimer's disease (adAD). A similar time scale is lacking for sporadic Alzheimer's disease (sAD). The purpose was to design and validate a time scale in YECO for patients with sAD in relation to CSF and PET biomarkers.

METHODS

Patients diagnosed with Alzheimer's disease (AD, n = 48) or mild cognitive impairment (MCI, n = 46) participated in the study. They underwent a standardized clinical examination at the Memory clinic, Karolinska University Hospital, Stockholm, Sweden, which included present and previous medical history, laboratory screening, cognitive assessment, CSF biomarkers (Aβ₄₂, total-tau, and p-tau), and an MRI of the brain. They were also assessed with two PET tracers, ¹¹C-Pittsburgh compound B and ¹⁸F-fluorodeoxyglucose. Assuming concordance of cognitive decline in sAD and adAD, YECO for these patients was calculated using equations for the relationship between cognitive performance, YECO, and years of education in adAD (Almkvist et al. J Int Neuropsychol Soc 23:195-203, 2017).

RESULTS

The mean current point of disease progression was 3.2 years after the estimated clinical onset in patients with sAD and 3.4 years prior to the estimated clinical onset in patients with MCI, as indicated by the median YECO from five cognitive tests. The associations between YECO and biomarkers were significant, while those between chronological age and biomarkers were nonsignificant. The estimated disease onset (chronological age minus YECO) followed a bimodal distribution with frequency maxima before (early-onset) and after (late-onset) 65 years of age. The early- and late-onset subgroups differed significantly in biomarkers and cognition, but after control for YECO, this difference disappeared for all except the APOE e4 gene (more frequent in early- than in late-onset).

CONCLUSIONS

A novel time scale in years of disease progression based on cognition was designed and validated in patients with AD using CSF and PET biomarkers. Two early- and late-disease onset subgroups were identified differing with respect to APOE e4.

Associations Between Thinner Retinal Neuronal Layers and Suboptimal Brain Structural Integrity in a Middle-Aged Cohort

Purpose

The retina has potential as a biomarker of brain health and Alzheimer's disease (AD) because it is the only part of the central nervous system which can be easily imaged and has advantages over brain imaging technologies. Few studies have compared retinal and brain measurements in a middle-aged sample. The objective of our study was to investigate whether retinal neuronal measurements were associated with structural brain measurements in a middle-aged population-based cohort.

Participants and Methods

Participants were members of the Dunedin Multidisciplinary Health and Development Study (n=1037; a longitudinal cohort followed from birth and at ages 3, 5, 7, 9, 11, 13, 15, 18, 21, 26, 32, 38, and most recently at age 45, when 94% of the living Study members participated). Retinal nerve fibre layer (RNFL) and ganglion cell-inner plexiform layer (GC-IPL) thickness were measured by optical coherence tomography (OCT). Brain age gap estimate (brainAGE), cortical surface area, cortical thickness, subcortical grey matter volumes, white matter hyperintensities, were measured by magnetic resonance imaging (MRI).

Results

Participants with both MRI and OCT data were included in the analysis (RNFL n=828, female n=413 [49.9%], male n=415 [50.1%]; GC-IPL n=825, female n=413 [50.1%], male n=412 [49.9%]). Thinner retinal neuronal layers were associated with older brain age, smaller cortical surface area, thinner average cortex, smaller subcortical grey matter volumes, and increased volume of white matter hyperintensities.

Conclusion

These findings provide evidence that the retinal neuronal layers reflect differences in midlife structural brain integrity consistent with increased risk for later AD, supporting the proposition that the retina may be an early biomarker of brain health.

Friend or Foe? Defining the Role of Glutamate in Aging and Alzheimer’s Disease

Aging is a naturally occurring decline of physiological processes and biological pathways that affects both the structural and functional integrity of the body and brain. These physiological changes reduce motor skills, executive function, memory recall, and processing speeds. Aging is also a major risk factor for multiple neurodegenerative disorders including Alzheimer’s disease (AD). Identifying a biomarker, or biomarkers, that signals the transition from physiological to pathological aging would aid in earlier therapeutic options or interventional strategies. Considering the importance of glutamate signaling in synaptic plasticity, motor movement, and cognition, this neurotransmitter serves as a juncture between cognitive health and disease. This article discusses glutamatergic signaling during physiological aging and the pathological changes observed in AD patients. Findings from studies in mouse models of successful aging and AD are reviewed and provide a biological context for this transition. Finally, current techniques to monitor brain glutamate are highlighted. These techniques may aid in elucidating time-point specific therapeutic windows to modify disease outcome.

Low Serum Insulin-like Growth Factor-I Is Associated with Decline in Hippocampal Volume in Stable Mild Cognitive Impairment but not in Alzheimer's Disease

Background:

Serum insulin-like growth factor-I (IGF-I) has shown some association with hippocampal volume in healthy subjects, but this relation has not been investigated in stable mild cognitive impairment (sMCI) or Alzheimer’s disease (AD).

Objective:

At a single memory clinic, we investigated whether serum IGF-I was associated with baseline magnetic resonance imaging (MRI)-estimated brain volumes and longitudinal alterations, defined as annualized changes, up to 6 years of follow-up.

Methods:

A prospective study of patients with sMCI (n = 110) and AD (n = 60). Brain regions included the hippocampus and amygdala as well as the temporal, parietal, frontal, and occipital lobes, respectively.

Results:

Serum IGF-I was statistically similar in sMCI and AD patients (112 versus 123 ng/mL, p = 0.31). In sMCI, serum IGF-I correlated positively with all baseline MRI variables except for the occipital lobe, and there was also a positive correlation between serum IGF-I and the annualized change in hippocampal volume (r_s = 0.32, p = 0.02). Furthermore, sMCI patients having serum IGF-I above the median had lower annual loss of hippocampal volume than those with IGF-I below the median (p = 0.02). In contrast, in AD patients, IGF-I did not associate with baseline levels or annualized changes in brain volumes.

Conclusion:

In sMCI patients, our results suggest that IGF-I exerted neuroprotective effects on the brain, thereby maintaining hippocampal volume. In AD, serum IGF-I did not associate with brain volumes, indicating that IGF-I could not induce neuroprotection in this disease. This supports the notion of IGF-I resistance in AD.

Higher thyroid function is associated with accelerated hippocampal volume loss in Alzheimer's disease

BACKGROUND

In epidemiological studies, higher thyroid hormone (TH) levels have been associated with lower brain volume and increased risk of Alzheimer's disease (AD) in elderly individuals. However, the relationships between serum THs and hippocampal atrophy rates have previously not been investigated.

METHODS

A prospective study of patients with AD (n = 55), stable mild cognitive impairment (sMCI; n = 84) and healthy controls (n = 29) recruited at a single memory clinic. We investigated whether serum THs were associated with magnetic resonance imaging (MRI)-estimated hippocampal volumes at baseline and with longitudinal alterations, defined as annualized percent changes.

RESULTS

Serum levels of free triiodothyronine (FT3) and FT3/free thyroxine (FT4) ratio were reduced in AD and sMCI patients compared with the controls (p < 0.05). Hierarchical linear regression analyses showed that higher serum FT3/FT4 ratio was associated with greater baseline hippocampal volume in all study groups. Only in AD patients, higher serum FT4 was associated with lower baseline volume of the left hippocampus. Finally, exclusively in the AD group, higher serum levels of FT3 and FT3/FT4 ratio, and lower serum TSH levels, were associated with greater annual hippocampal volume loss.

CONCLUSIONS

In all study groups, FT3/FT4 ratio was related to baseline hippocampal volume. However, only in AD patients, higher levels of THs were associated with greater annual loss of hippocampal volume, suggesting that excessive TH levels exert a deleterious effect on the hippocampus in the presence of existing AD neuropathology.

Prediction of Chronological Age in Healthy Elderly Subjects with Machine Learning from MRI Brain Segmentation and Cortical Parcellation

Normal aging is associated with changes in volumetric indices of brain atrophy. A quantitative understanding of age-related brain changes can shed light on successful aging. To investigate the effect of age on global and regional brain volumes and cortical thickness, 3514 magnetic resonance imaging scans were analyzed using automated brain segmentation and parcellation methods in elderly healthy individuals (69–88 years of age). The machine learning algorithm extreme gradient boosting (XGBoost) achieved a mean absolute error of 2 years in predicting the age of new subjects. Feature importance analysis showed that the brain-to-intracranial-volume ratio is the most important feature in predicting age, followed by the hippocampi volumes. The cortical thickness in temporal and parietal lobes showed a superior predictive value than frontal and occipital lobes. Insights from this approach that integrate model prediction and interpretation may help to shorten the current explanatory gap between chronological age and biological brain age.

Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer’s Disease

Background: Late-onset Alzheimer’s disease (LOAD) and early-onset Alzheimer’s disease (EOAD) are different subtypes of AD. This study aimed to build and validate radiomics models of the hippocampus for EOAD and young controls (YCs), LOAD and old controls (OCs), as well as EOAD and LOAD.

Methods: Thirty-six EOAD patients, 36 LOAD patients, 36 YCs, and 36 OCs from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database were enrolled and allocated to training and test sets of the EOAD-YC groups, LOAD-OC groups, and EOAD-LOAD groups. Independent external validation sets including 15 EOAD patients, 15 LOAD patients, 15 YCs, and 15 OCs from Shanghai Mental Health Center were constructed, respectively. Bilateral hippocampal segmentation and feature extraction were performed for each subject, and the least absolute shrinkage and selection operator (LASSO) method was used to select radiomic features. Support vector machine (SVM) models were constructed based on the identified features to distinguish EOAD from YC subjects, LOAD from OC subjects, and EOAD from LOAD subjects. The areas under the receiver operating characteristic curves (AUCs) were used to evaluate the performance of the models.

Results: Three, three, and four features were selected for EOAD and YC subjects, LOAD and OC subjects, and EOAD and LOAD subjects, respectively. The AUC and accuracy of the SVM model were 0.90 and 0.77 in the test set and 0.91 and 0.87 in the validation set for EOAD and YC subjects, respectively; for LOAD and OC subjects, the AUC and accuracy were 0.94 and 0.86 in the test set and 0.92 and 0.78 in the validation set, respectively. For the SVM model of EOAD and LOAD subjects, the AUC was 0.87 and the accuracy was 0.79 in the test set; additionally, the AUC was 0.86 and the accuracy was 0.77 in the validation set.

Conclusion: The findings of this study provide insights into the potential of hippocampal radiomic features as biomarkers to diagnose EOAD and LOAD. This study is the first to show that SVM classification analysis based on hippocampal radiomic features is a valuable method for clinical applications in EOAD.

Validity and reliability of the medial temporal lobe atrophy scale in a memory clinic population

Background

Visual rating of medial temporal lobe atrophy (MTA) is often performed in conjunction with dementia workup. Most prior studies involved patients with known or probable Alzheimer’s disease (AD). This study investigated the validity and reliability of MTA in a memory clinic population.

Methods

MTA was rated in 752 MRI examinations, of which 105 were performed in cognitively healthy participants (CH), 184 in participants with subjective cognitive impairment, 249 in subjects with mild cognitive impairment, and 214 in patients with dementia, including AD, subcortical vascular dementia and mixed dementia. Hippocampal volumes, measured manually or using FreeSurfer, were available in the majority of cases. Intra- and interrater reliability was tested using Cohen’s weighted kappa. Correlation between MTA and quantitative hippocampal measurements was ascertained with Spearman’s rank correlation coefficient. Moreover, diagnostic ability of MTA was assessed with receiver operating characteristic (ROC) analysis and suitable, age-dependent MTA thresholds were determined.

Results

Rater agreement was moderate to substantial. MTA correlation with quantitative volumetric methods ranged from -0.20 (p< 0.05) to -0.68 (p < 0.001) depending on the quantitative method used. Both MTA and FreeSurfer are able to distinguish dementia subgroups from CH. Suggested age-dependent MTA thresholds are 1 for the age group below 75 years and 1.5 for the age group 75 years and older.

Conclusions

MTA can be considered a valid marker of medial temporal lobe atrophy and may thus be valuable in the assessment of patients with cognitive impairment, even in a heterogeneous patient population.

Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer's disease from healthy controls

OBJECTIVES

The present study aims to compare early-onset Alzheimer's disease (EOAD) patients with healthy controls (HC), and late-onset Alzheimer's disease (LOAD) patients using resting-state delta, theta, alpha, and beta oscillations and provide a cut-off score of alpha/theta ratio to discriminate individuals with EOAD and young HC.

METHODS

Forty-seven individuals with EOAD, 51 individuals with LOAD, and demographically-matched 49 young and 51 older controls were included in the study. Spectral-power analysis using Fast-Fourier Transformation (FFT) is performed on resting-state electroencephalography (EEG) data. Delta, theta, alpha, and beta oscillations compared between groups and Receiver Operating Characteristic (ROC) curve analysis was conducted.

RESULTS

Compared to healthy controls individuals with EOAD showed an increase in slow frequency bands and a decrease in fast frequency bands. Frontal alpha/theta power ratio is the best discriminating value between EOAD and young HC with the sensitivity and specificity greater than 80% with area under the curve (AUC) 0.881.

CONCLUSIONS

EOAD display more widespread and severe electrophysiological abnormalities than LOAD and HC which may reflect more pronounced pathological burden and cholinergic deficits in EOAD. Additionally, the alpha/theta ratio can discriminate EOAD and young HC successfully.

SIGNIFICANCE

This study is the first to report that resting-state EEG power can be a promising marker for diagnostic accuracy between EOAD and healthy controls.

Alterations in resting-state network dynamics along the Alzheimer's disease continuum

Human brain activity is intrinsically organized into resting-state networks (RSNs) that transiently activate or deactivate at the sub-second timescale. Few neuroimaging studies have addressed how Alzheimer's disease (AD) affects these fast temporal brain dynamics, and how they relate to the cognitive, structural and metabolic abnormalities characterizing AD. We aimed at closing this gap by investigating both brain structure and function using magnetoencephalography (MEG) and hybrid positron emission tomography-magnetic resonance (PET/MR) in 10 healthy elders, 10 patients with subjective cognitive decline (SCD), 10 patients with amnestic mild cognitive impairment (aMCI) and 10 patients with typical Alzheimer's disease with dementia (AD). The fast activation/deactivation state dynamics of RSNs were assessed using hidden Markov modeling (HMM) of power envelope fluctuations at rest measured with MEG. Correlations were sought between temporal properties of HMM states and participants' cognitive test scores, whole hippocampal grey matter volume and regional brain glucose metabolism. The posterior default-mode network (DMN) was less often activated and for shorter durations in AD patients than matched healthy elders. No significant difference was found in patients with SCD or aMCI. The time spent by participants in the activated posterior DMN state did not correlate significantly with cognitive scores, nor with the whole hippocampal volume. However, it correlated positively with the regional glucose consumption in the right dorsolateral prefrontal cortex (DLPFC). AD patients present alterations of posterior DMN power activation dynamics at rest that identify an additional electrophysiological correlate of AD-related synaptic and neural dysfunction. The right DLPFC may play a causal role in the activation of the posterior DMN, possibly linked to the occurrence of mind wandering episodes. As such, these data might suggest a neural correlate of the decrease in mind wandering episodes reported in pathological aging.

Altered thyroid hormone profile in patients with Alzheimer's disease

BACKGROUND

Epidemiological studies have linked higher levels of thyroid hormones (THs) to increased risk of Alzheimer's disease (AD), whereas in advanced AD, THs have been unchanged or even decreased. In early AD dementia, little is known whether THs are related to AD neuropathology or brain morphology.

METHODS

This was a cross-sectional study of 36 euthyroid AD patients and 34 healthy controls recruited at a single memory clinic. Levels of THs were measured in serum and cerebrospinal fluid (CSF). In addition, we determined AD biomarkers (amyloid-β_1-42, total tau and phosphorylated tau) in CSF and hippocampal and amygdalar volumes using magnetic resonance imaging.

RESULTS

Serum free thyroxine (FT4) levels were elevated, whereas serum free triiodothyronine (FT3)/FT4 and total T3 (TT3)/total T4 (TT4) ratios were decreased, in AD patients compared to controls. In addition, serum TT4 was marginally higher in AD (p = 0.05 vs. the controls). Other TH levels in serum as well as CSF concentrations of THs were similar in both groups, and there were no correlations between THs and CSF AD biomarkers. However, serum FT3 correlated positively with left amygdalar volume in AD patients and serum TT3 correlated positively with left and right hippocampal volume in controls.

CONCLUSIONS

Thyroid hormones were moderately altered in mild AD dementia with increased serum FT4, and in addition, the reduced T3/T4 ratios may suggest decreased peripheral conversion of T4 to T3. Furthermore, serum T3 levels were related to brain structures involved in AD development.

Human Gut-Microbiota Interaction in Neurodegenerative Disorders and Current Engineered Tools for Its Modeling

The steady increase in life-expectancy of world population, coupled to many genetic and environmental factors (for instance, pre- and post-natal exposures to environmental neurotoxins), predispose to the onset of neurodegenerative diseases, whose prevalence is expected to increase dramatically in the next years. Recent studies have proposed links between the gut microbiota and neurodegenerative disorders such as Alzheimer's and Parkinson's diseases. Human body is a complex structure where bacterial and human cells are almost equal in numbers, and most microbes are metabolically active in the gut, where they potentially influence other target organs, including the brain. The role of gut microbiota in the development and pathophysiology of the human brain is an area of growing interest for the scientific community. Several microbial-derived neurochemicals involved in the gut-microbiota-brain crosstalk seem implicated in the biological and physiological basis of neurodevelopment and neurodegeneration. Evidence supporting these connections has come from model systems, but there are still unsolved issues due to several limitations of available research tools. New technologies are recently born to help understanding the causative role of gut microbes in neurodegeneration. This review aims to make an overview of recent advances in the study of the microbiota-gut-brain axis in the field of neurodegenerative disorders by: (a) identifying specific microbial pathological signaling pathways; (b) characterizing new, advanced engineered tools to study the interactions between human cells and gut bacteria.

The basis of cellular and regional vulnerability in Alzheimer's disease

Alzheimer's disease (AD) differentially and specifically affects brain regions and neuronal cell types in a predictable pattern. Damage to the brain appears to spread and worsens with time, taking over more regions and activating multiple stressors that can converge to promote vulnerability of certain cell types. At the same time, other cell types and brain regions remain intact in the face of this onslaught of neuropathology. Although neuropathologic descriptions of AD have been extensively expanded and mapped over the last several decades, our understanding of the mechanisms underlying how certain regions and cell populations are specifically vulnerable or resistant has lagged behind. In this review, we detail what is known about the selectivity of local initiation of AD pathology in the hippocampus, its proposed spread via synaptic connections, and the diversity of clinical phenotypes and brain atrophy patterns that may arise from different fibrillar strains of pathologic proteins or genetic predispositions. We summarize accumulated and emerging knowledge of the cellular and molecular basis for neuroanatomic selectivity, consider potential disease-relevant differences between vulnerable and resistant neuronal cell types and isolate molecular markers to identify them.

Glial S100A6 Degrades β-amyloid Aggregation through Targeting Competition with Zinc Ions

Evidence has been accumulating that zinc ions can trigger β-amyloid (Aβ) deposition and senile plaque formation in the brain, a pathological hallmark of Alzheimer's disease (AD). Chelating zinc inhibits Aβ aggregation and may hold promise as a therapeutic strategy for AD. S100A6 is an acidic Ca²⁺/Zn²⁺-binding protein found only in a small number of astrocytes in the normal brain. However, in the AD brain, S100A6 is highly expressed in astrocytes around Aβ plaques. The role of the astrocytic S100A6 upregulation in AD is unknown. In the present study, we examined the effects of S100A6 on Aβ plaques and intracellular zinc levels in a mouse model of AD. Chronic exposure to zinc increased Aβ deposition and S100A6 expression, both reversible by the zinc chelator clioquinol, in the brains of amyloid precursor protein/presenilin 1 (APP/PS1) transgenic mice. To examine whether exogenous S100A6 could induce Aβ plaque disaggregation through competition for zinc in vitro, we incubated APP/PS1 mouse brain sections with recombinant human S100A6 protein or co-incubated them with human S100A6-expressing cells. Both treatments efficiently reduced the Aβ plaque burden in situ. In addition, treatment with exogenous S100A6 protected cultured COS-7 cells against zinc toxicity. Our results show for the first time that increased S100A6 levels correlate with both Aβ disaggregation and decrease of Aβ plaque-associated zinc contents in brain sections with AD-like pathology. Astrocytic S100A6 in AD may protect from Aβ deposition through zinc sequestration.

Perifovea retinal thickness as an ophthalmic biomarker for mild cognitive impairment and early Alzheimer's disease

Introduction

The aim of this study was to investigate retinal thickness as a biomarker for identifying patients with mild cognitive impairment (MCI) and Alzheimer's disease (AD).

Methods

The retinal thickness, utilizing the spectral domain optical coherence tomography, was compared among 73 patients with AD, 51 patients with MCI, 67 cognitive normal control (NC) subjects.

Results

The retinal thickness of ganglion cell complex and peripapillary retinal nerve fiber layer decreased in both AD and MCI patients, in comparison with NC subjects (AD vs. NC, P < .01; MCI vs. NC, P < .01). The inner retinal layers in macular area in MCI exhibited significant thinning compared with NC (P < .001). Remarkable association was found between the retinal thickness and brain volume (P < .05). Better correlation was seen between the inner perifovea retinal thickness and the hippocampal and entorhinal cortex volume (r: 0.427–0.644, P < .01).

Discussion

The retinal thickness, especially the inner retinal layer thickness, is a potentially early AD marker indicating neurodegeneration.