Dataset Information

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

ABSTRACT:

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.

SUBMITTER: Tao R

PROVIDER: S-EPMC6558027 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Publications

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

Tao Rui R Lu Zhaozeng Z Ding Ding D Fu Shuhao S Hong Zhen Z Liang Xiaoniu X Zheng Li L Xiao Yiqin Y Zhao Qianhua Q

Alzheimer's & dementia (Amsterdam, Netherlands) 20190606

<h4>Introduction</h4>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).<h4>Methods</h4>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.<h4>Results</h4>The retinal thickness of ganglion cell complex and peripapillary retinal nerve fiber layer decr ...[more]

PMID: 31206006

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Project description:Multiple biomarkers can capture different facets of Alzheimer's disease. However, statistical models of biomarkers to predict outcomes in Alzheimer's rarely model nonlinear interactions between these measures. Here, we used Gaussian Processes to address this, modelling nonlinear interactions to predict progression from mild cognitive impairment (MCI) to Alzheimer's over 3 years, using Alzheimer's Disease Neuroimaging Initiative (ADNI) data. Measures included: demographics, APOE4 genotype, CSF (amyloid-β42, total tau, phosphorylated tau), [18F ]florbetapir, hippocampal volume and brain-age. We examined: (a) the independent value of each biomarker; and (b) whether modelling nonlinear interactions between biomarkers improved predictions. Each measured added complementary information when predicting conversion to Alzheimer's. A linear model classifying stable from progressive MCI explained over half the variance (R2 = 0.51, p < .001); the strongest independently contributing biomarker was hippocampal volume (R2 = 0.13). When comparing sensitivity of different models to progressive MCI (independent biomarker models, additive models, nonlinear interaction models), we observed a significant improvement (p < .001) for various two-way interaction models. The best performing model included an interaction between amyloid-β-PET and P-tau, while accounting for hippocampal volume (sensitivity = 0.77, AUC = 0.826). Closely related biomarkers contributed uniquely to predict conversion to Alzheimer's. Nonlinear biomarker interactions were also implicated, and results showed that although for some patients adding additional biomarkers may add little value (i.e., when hippocampal volume is high), for others (i.e., with low hippocampal volume) further invasive and expensive examination may be warranted. Our framework enables visualisation of these interactions, in individual patient biomarker 'space', providing information for personalised or stratified healthcare or clinical trial design.

Project description:BackgroundAlzheimer's disease (AD) biomarkers are emerging as critically important for disease detection and monitoring. Most biomarkers are obtained through invasive, resource-intense procedures. A cognitive marker, intra-individual cognitive variability (IICV) may provide an alternative or adjunct marker of disease risk for individuals unable or disinclined to undergo lumbar puncture.ObjectiveTo contrast risk of incident AD and mild cognitive impairment (MCI) associated with IICV to risk associated with well-established biomarkers: cerebrospinal fluid (CSF) phosphorylated tau protein (p-tau181) and amyloid-β 42 (Aβ42) peptide.MethodsDispersion in cognitive performance, IICV, was estimated with a published algorithm, and included Trail Making Test A and B, Rey Auditory Verbal Learning Test (RAVLT), and the American National Adult Reading Test (ANART). CSF biomarkers were expressed as a ratio: p-tau181/Aβ42, wherein high values signified pathognomonic profiles. Logistic regression models included longitudinal data from 349 Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who completed lumbar puncture. All subjects were cognitively healthy (n = 105) or diagnosed with MCI (n = 244) at baseline. We examined odds of conversion associated with baseline elevations in IICV and/or ratio of CSF p-tau181/Aβ42.ResultsWhen included in models alone or in combination with CSF p-tau181/Aβ42, one standard IICV unit higher was associated with an estimated odds ratio for incident AD or MCI of 2.81 (95% CI: 1.83-4.33) in the most inclusive sample, and an odds ratio of 3.41 (95% CI: 2.03-5.73) when restricted to participants with MCI. Iterative analyses suggested that IICV independently improved model fit even when individual index components were included in comparative models.ConclusionsThese analyses provide preliminary support for IICV as a marker of incident AD and MCI. This easily-disseminated, non-invasive marker compared favorably to well-established CSF biomarkers.

Project description:<h4><strong>INTRODUCTION </strong>Alzheimer's disease is a prevalent disease with a heavy global burden and is suggested to be a metabolic disease in the brain in recent years. The metabolome is considered to be the most promising phenotype which reflects changes in genetic, transcript, and protein profiles as well as environmental effects. Aiming to obtain a comprehensive understanding and convenient diagnosis of MCI and AD from another perspective, researchers are working on AD metabolomics. Urine is more convenient which could reflect the change of disease at an earlier stage. Thus, we conducted a cross-sectional study to investigate novel diagnostic panels.</h4><p><strong>METHODS </strong>We first enrolled participants from China-Japan Friendship Hospital from April 2022 to November 2022, collected urine samples and conducted an LC-MS/MS analysis. In parallel, clinical data were collected and clinical examinations were performed. After statistical and bioinformatics analyzes, significant risk factors and differential urinary metabolites were determined. We attempt to investigate diagnostic panels based on machine learning including LASSO and SVM.</p><p><strong>RESULTS </strong>57 AD patients, 43 MCI patients and 62 CN subjects were enrolled. A total of 2,140 metabolites were identified among which 125 significantly differed between the AD and CN groups, including 46 upregulated ones and 79 downregulated ones. In parallel, there were 93 significant differential metabolites between the MCI and CN groups, including 23 upregulated ones and 70 downregulated ones. AD diagnostic panel (30 metabolites+ age + APOE) achieved an AUC of 0.9575 in the test set while MCI diagnostic panel (45 metabolites+ age + APOE) achieved an AUC of 0.7333 in the test set. Atropine, S-Methyl-L-cysteine-S-oxide, D-Mannose 6-phosphate (M6P), Spiculisporic Acid, N-Acetyl-L-methionine, 13,14-dihydro-15-keto-tetranor Prostaglandin D2, Pyridoxal 5'-Phosphate (PLP) and 17(S)-HpDHA were considered valuable for both AD and MCI diagnosis and defined as hub metabolites. Besides, diagnostic metabolites were weakly correlated with cognitive functions.</p><p><strong>DISCUSSION </strong>In conclusion, the procedure is convenient, non-invasive, and useful for diagnosis, which could assist physicians in differentiating AD and MCI from CN. Atropine, M6P and PLP were evidence-based hub metabolites in AD.</p>

Dataset Information

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

Introduction

Methods

Results

Discussion

Publications

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

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