Introduction
Alzheimer’s disease (AD) is the most common form of dementia and is currently a major medical and social burden on the aging population worldwide (Mayeux and Stern, 2012). Current estimates according to population-based studies suggest that one in three individuals born in developed countries will develop AD during their life (Lewis, 2015).
The exact mechanism behind AD pathology remains ill-defined, however, it is proposed that AD is mainly associated with extracellular deposits of amyloid-β (Aβ) plaques, loss of neurons, intracellular accumulation of neurofibrillary tangles, and abnormal presence of the intracellular tau protein (Gauthier et al., 2010; Wang et al., 2017). Recent evidence has also pointed to AD sharing molecular overlap of neuropathology with glaucoma and age-related macular degeneration. The buildup of Aβ plaques in AD leads to chronic activation of inflammatory pathways and thus increasing reactive oxygen species which together contribute to the hallmark characteristics of AD neuropathology in the brain and the retina (Ashok et al., 2020). Most of the time, AD is diagnosed relatively late in the disease course, likely when extensive and irreversible damage has already occurred, with a definitive diagnosis only being made through histopathologic examination, which requires either an invasive brain biopsy or postmortem examination. Clinical criteria combined with neuroimaging techniques for the detection of abnormal biomarkers of Aβ deposition, including cerebrospinal fluid (CSF) or positron emission tomography (PET) whilst helpful are not without limitations. PET imaging of the brain is expensive and therefore may not be offered for all patients while CSF testing is invasive (den Haan et al., 2017a). In contrast, the retina and retinal microvasculature can be directly imaged and accessed due to their anatomical location. Examining the retinal tissue has the potential to provide a unique method and technique to quantify AD-related changes (Cheung et al., 2014). The retina has long been considered a “window” to study disorders in the central nervous system, as it is an extension of the brain embryologically anatomically, and physiologically and thus is the only central nervous system structure that can be imaged non-invasively at subcellular levels (Patton et al., 2005; London et al., 2013). Recent evidence has revealed that the eyes and brain are both affected by AD and that pathological changes in both of these tissues can be correlated (Koronyo et al., 2017). Previous studies have provided evidence for retinal measures through optical coherence tomography (OCT) namely retinal nerve fiber layer (RNFL) thickness and ganglion cell inner plexiform layer (GCIPL) thickness as potentially useful in highlighting changes in AD and may serve as useful biomarkers of AD (Chan et al., 2019; Mammadova et al., 2020; Zhang et al., 2021).
Technological advancements in OCT imaging have now allowed for quantitative analyses of deeper and more individualized layers of the retina to include angiographic capabilities, termed OCT angiography (OCTA). OCTA enables an in-depth view of the retinal microvascular network by not only measuring vasculature at distinct depths (superficial and deep) but also measuring the size, blood flow, and directionality in real-time all without the need for injection of a fluorescein dye (Ling et al., 2020). The degree of light absorption and scattering serves as the basis for imaging specific retinal layers. When compared to earlier conventional time domain OCT devices, spectral domain OCT has provided a faster scanning speed and higher resolution. Most recently swept-source OCT has been shown as having more accuracy in quantitative segmented retinal analysis with increased speed and resolution (Zhang et al., 2021). As AD is a disease with vascular changes, OCTA may prove to be an invaluable asset in reflecting changes occurring within the retina early in the disease progression. There has been increasing interest in the field of OCTA with studies already illustrating associations with changes in vasculature between healthy controls and AD patients (Bulut et al., 2018; Yoon et al., 2019a). In contrast, some studies have also revealed no differences in vascular density between AD and healthy controls (Querques et al., 2019). We, therefore, conducted this meta-analysis to investigate the association of various OCT parameters with AD and whether retinal measurements can be used to differentiate between AD and control subjects.
Methods
This study was conducted by adhering to the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analyses) statement (Moher et al., 2015) and Cochrane’s Handbook guidelines. The literature search was created to answer the question: ‘Is there an association between OCT and OCTA measurements within the retina between controls and AD patients?’
Literature search
Two independent Reviewers (the authors SS and SA) conducted a literature search using medical subject headings (MeSH) terms. PubMed, Web of Science, and Google Scholar were systematically searched without language and date restrictions till March 2022 (Figure 1). Search terms used included “Alzheimer’s Disease”, “Retinal nerve fiber layer (RNFL)”, and “MMSE” alone and in combination. The literature database search was periodically updated using additional keywords such as “optical coherence tomography (OCT), magnetic resonance imaging (MRI), and “vessel density (VD). In addition to database searching using keywords, we also manually reviewed and searched the reference lists of primarily selected articles to not miss relevant articles. Study search criteria are shown in Additional file 1.
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Figure 1: Flowchart of literature search.
Additional file 1: Study design-specific critical appraisal tools.
Study selection
Studies meeting the following criteria were included in this meta-analysis: (1) studies with a case-control or cross-sectional design; (2) studies recruiting subjects with AD or appropriate aged-matched controls; (3) studies providing the data of RNFL thickness either (total, superior, inferior, nasal, and temporal) either alone or in combination, original data should contain the mean and standard deviation; (4) studies providing the data of macular parameters either (macular thickness, macular volume, foveal thickness, and GCIPL thickness) either alone or in combination., original data should provide the mean and standard deviation; (5) studies examining OCTA parameters including superficial vessel density (SVD), deep vessel density (DVD) and foveal avascular zone (FAZ); (6) studies with original data including mean and standard deviation; (7) subjects should be diagnosed according to established diagnostic systems (e.g., Diagnostic and statistical manual of mental disorders (DSM-III, DSM-IV, DSM-VI), National Institute of Neurological and Communicative Disorders and Stroke and the Alzheimer’s Disease and Related Disorders Association (NINDS-ADRDA) and Peterson criteria. Longitudinal studies were included with baseline data being extracted and used for analysis.
Studies were excluded if: (1) duplicate publications across databases; (2) case reports, animal studies, abstracts, conference presentations, or letters to the editor; (3) inadequate outcomes or data that could not be extracted; (4) purpose did not meet the broad goal of this meta-analysis.
Data extraction and quality assessment
Data were extracted using a two-step process. In the first phase, two investigators (the authors SS and TS) screened article titles from the computerized search. In the second phase, three investigators (the authors SS, TS, and SA) extracted data by assessing full-text articles and entering relevant data into a customized database. The extracted data included author, year, study title, year of publication, journal of publication, study design, country, sample size (AD, mild cognitive impairment, controls), diagnostic criteria, age, sex, retinal imaging technique (OCT apparatus), OCTA apparatus and cognitive scoring scales Mini-Mental State Examination, Geriatric Depression Scale and the Clinical Dementia Rating. The extracted clinical data included means with standard deviations and hazard ratios if applicable. The parapapillary RNFL thickness parameters evaluated in these studies were average thickness (360° measurement), temporal quadrant thickness (316°–45°), superior quadrant thickness (46°–135°), nasal quadrant thickness (136°–225°) and inferior quadrant thickness (226°–315°). Macular thickness data was reported among studies using two primary OCT imaging methods; The (512 × 128) Macular cube protocol or the Early Treatment Diabetic Retinopathy Study grid and the glaucoma analysis grid. The Early Treatment Diabetic Retinopathy Study grid consists of three concentric circles with diameters of 1, 3, and 6 mm. All data were extracted from the published studies. Disagreements in data extraction were resolved by consensus among members of the review team.
Data analysis
The Joanna Briggs Institute Critical Appraisal tools (https://jo annabriggs.org/critical-appraisal-tools) (Munn et al., 2014) were used to assess the study quality. Each tool was composed of eight to eleven domains depending on the study design (Additional file 1). In addition, each study’s methodology, study design, and reporting standards were checked to assess the quality of the studies included in the review. Two reviewers (the authors SS and TS) conducted the assessment and disagreements were solved by discussion and concerns regarding applicability with the research team. The methodology was examined for appropriate patient inclusion, a complete description of technical and research methods used, and if confounding factors were analyzed. The study design was rated as either case-control or longitudinal. For result reporting, we assessed whether studies fully described patient results and diagnoses as per standard criteria, imaging tool information, and use of a control group or nil.
Statistical analysis for this review was completed using Review Manager Version 5.3 (Cochrane Collaboration, Oxford, UK). Key data from each study, including retinal nerve fiber values, macular thickness, and OCTA parameters were pooled into a weighted summary. Relative risks with 95% CI were analyzed to determine the relationship between AD and control patients. Continuous variables were assessed in terms of standardized mean differences (SMD) with 95% confidence intervals (CI). The heterogeneity among studies was analyzed using the chi2 test based on values of I2. I2 values range from 0 to 100% with 25%, 50%, and 75% indicating low, moderate, and high heterogeneity respectively. Since several OCT and OCTA parameters exist which can be assessed on various machines (Table 1), the SMD was used as a summary statistic in this analysis. This method is particularly useful when studies assess the same outcome but measure it in different ways. Publication bias was assessed by visual observation of the generated funnel plots for each measured parameter (Additional file 2).
Table 1: Demographic characteristics of the included studies
Additional file 2: Critical appraisal of the included studies.
Results
Search results
The process used to search the literature is summarized in Figure 1. A total of 187 studies were initially extracted from all databases searching (Google Scholar, PubMed, and Web of Science) after duplicates had been removed and records excluded for non-relevance based on abstract and study title (Figure 1). 187 studies were then assessed for eligibility with a total of 73 studies identified from the literature that examined RNFL thickness in controls and Alzheimer’s disease patients. 28 out of the 73 studies also examined mild cognitive impairment (MCI) patients in addition to the AD and controls (however for this meta-analysis focus was placed on AD and controls). Analysis was also conducted on macular parameters including total macular thickness between AD and controls, the macular volume between AD and controls, GCIPL thickness between AD and controls, and foveal thickness between AD and controls. OCTA angiography including SVD, DVD, and FAZ thickness was also examined in the included studies. Overall, from the 73 studies we included a total of 2249 patients with AD and 3601 controls (Figure 2). 1165 MCI patients were also examined in the reported studies, however, were not analyzed for this meta-analysis. For the quadrant RNFL analysis, we identified 36 studies suitable for analysis (superior 32, inferior 33, nasal 36, and temporal 36) (Table 1).
Figure 2: Venn diagram of subjects included in this meta-analysis.AD: Alzheimer’s disease.
Study characteristics and quality evaluation
Characteristics of the included studies are summarised in (Tables 1 and 2). Included studies were published between 2001 and 2021 with the majority being conducted in the USA, Italy, Spain, China, Turkey, Poland, Korea, and Australia respectively (Table 3). Of the included studies the sample size varied from 9 to 324 AD patients and 8 to 613 HCs with the mean age of participants ranging from 60 to 79 years. Criteria for AD and dementia diagnosis were based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-III, IV, -V, and -VI), National Institute of Neurological and Communicative Disorders and Stroke Alzheimer’s Disease and Related Disorders Association (NINCDS-ADRA) criteria or a combination of both. Studies also varied in terms of the subject groups examined for comparison. The majority of included studies reported patients with AD and age-matched controls, whilst twenty-one studies (Paquet et al., 2007; Kesler et al., 2011; Liu et al., 2013; Ascaso et al., 2014; Cheung et al., 2015; Gao et al., 2015; Oktem et al., 2015; Pillai et al., 2016; Ferrari et al., 2017; Kwon et al., 2017; Jiang et al., 2018b; Uchida et al., 2018; Kim and Kang, 2019; Tao et al., 2019; Yoon et al., 2019a; Zabel et al., 2019a; Ito et al., 2020; Szegedi et al., 2020; Wu et al., 2020; Carazo-Barrios et al., 2021; Chua et al., 2021) included an additional subset of patients reported as MCI or subjective memory complaints. Finally, regarding OCT/OCTA parameters, the vast majority of studies evaluated total RNFL, sectoral RNFL, or a combination of both in addition to macular thickness and foveal thickness. Twelve OCTA studies (Bulut et al., 2018; Lahme et al., 2018; Wang et al., 2018; den Haan et al., 2019a; Querques et al., 2019; Sadda et al., 2019; Yoon et al., 2019b; Zabel et al., 2019b; Ahn et al., 2020; Chua et al., 2021; Li et al., 2021; Yan et al., 2021) primarily examined a host of vascular parameters with the vast majority exploring retinal vascular density superficial, deep and FAZ.
Table 2: Peripapillary RNFL in total and all quadrants in the included 53 studies
Table 3: Demographics and country distribution of the included 73 studies
Comparison of global RNFL and quadrants in AD and controls
We identified a total of 53 studies that evaluated RNFL thickness in AD and controls (2441 AD patients and 2128 controls). There was a significant reduction in global RNFL thickness in AD patients compared to HC (SMD = –0.79, 95% CI: –1.03 to –0.54, P < 0.00001; Figure 3). Significant heterogeneity was observed across the included studies (Chi2 = 618.28, P < 0.00001, I2 = 92%). Meta-analysis of each RNFL quadrant (superior, inferior, nasal, and temporal) indicated considerable heterogeneity across studies and significant differences in RNFL thickness between the two groups (AD and Control). Superior (SMD = –1.04, 95% CI: –1.47 to –0.60, P < 0.0001, I2 = 95%; Figure 4), inferior (SMD = –0.65, 95% CI: –0.97 to –0.34, P < 0.0001, I2= 91%; Figure 5), nasal (WMD = –0.34, 95% CI: –0.55 to –0.12, P = 0.002, I2 = 85%; Figure 6) and temporal (WMD = –0.52, 95% CI: –0.82 to –0.23, P = 0.0006, I2 = 92%; Figure 7).
Figure 3: Forrest plots of the pooled standardised mean difference (SMDs) on patients with AD and controls of global RNFL (μm) (n = 53 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity and overall inverse variance (IV) random effects model is used. AD: Alzheimer’s disease.
Figure 4: Forrest plots of the pooled standardized mean difference (SMDs) on patients with AD and controls of superior RNFL (μm) (n = 32 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer’s disease.
Figure 5: Forrest plots of the pooled standardized mean difference (SMDs) on patients with AD and controls of inferior RNFL (μm) (n = 33 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer’s disease.
Figure 6: Forrest plots of the pooled standardized mean difference (SMDs) on patients with AD and controls of Nasal RNFL (μm) (n = 36 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer’s disease.
Figure 7: Forrest plots of the pooled standardized mean difference (SMDs) on patients with AD and controls of temporal RNFL (μm) (n = 36 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer's disease.
Comparison of macular parameters in AD and controls
The macular region was examined and macular thickness, macular volume, foveal thickness and GCIPL were analyzed. Macular thickness was analyzed in 633 AD patients and 1376 controls. Macular thickness was reported to be lower in patients with AD compared with controls (pooled SMD = –0.44, 95% CI: –0.67 to –0.20, P = 0.0003, I2 = 71%; Figure 8A). Macular volume was analyzed in 533 AD patients and 685 controls. Macular volume was found to be slightly lower in AD compared with controls (pooled SMD = –0.41, 95% CI: –0.76 to –0.07, P = 0.02, I2 =82%; Figure 8B). Foveal thickness was analyzed in 881 AD and 977 controls. Foveal Thickness was lower in AD compared with controls (pooled SMD = –0.39, 95% CI: –0.58 to –0.19, P < 0.0001, I2 = 68%; Figure 8C). Finally, GCIPL was analyzed in 310 AD patients and 408 controls. The GCIPL layer was reported thinner in AD patients compared with controls (SMD = –1.26, 95% CI: –2.24 to –0.27, P = 0.01, I2 = 97%; Figure 8D).
Figure 8: Forrest plots of the pooled standardized mean difference (SMDs) on patients with AD and controls of Macular Thickness (A), Macular Volume (B), foveal thickness (C), and GCIPL (D) versus controls (n = 26 studies).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer's disease.
Comparison of OCTA measures: SVD, DVD, and FAZ in AD and controls
SVD was analyzed in 318 AD patients and 409 controls. The macular SVD was lower in patients with AD compared with controls (pooled SMD = –0.42, 95% CI: –0.68 to –0.17, P = 0.001, I2 = 60%; Figure 9A). DVD was analyzed in 211 AD patients and 219 controls. The DVD was lower in patients with AD compared with controls (pooled SMD: –0.46, 95% CI: –0.75 to –0.18, P = 0.001, I2 = 49%; Figure 9B). Heterogeneity across the studies was mild to moderate. Finally, the FAZ was analyzed in 226 AD patients and 306 controls. The FAZ was found to be larger in patients with AD compared with controls (SMD = 0.84, 95% CI: 0.17 to 1.51, P = 0.01; Figure 9C). High heterogeneity was found among the included studies (Chi2 = 70.79, P < 0.00001, I2 = 92%).
Figure 9: Forest plots of the pooled standardized mean differences (SMDs) on patients with Alzheimer’s disease (AD) and controls between SVD (A), DVD (B), and FAZ (C).Mean and standard deviation (SD) are reported with a 95% confidence interval (CI), heterogeneity, and overall inverse variance (IV) random effects model is used. AD: Alzheimer’s disease; DVD: deep vessel density; FAZ: foveal avascular zone; SVD: superficial vessel density.
Discussion
This meta-analysis was conducted to evaluate various OCT and OCTA retinal measurements that could be relevant for diagnosing AD, including global RNFL thickness, sectoral thickness (superior, inferior, nasal, temporal), total macular thickness, macular volume, foveal thickness, GCIPL, superficial vessel density, deep vessel density, and FAZ area.
The majority of studies that utilised OCT (Parisi et al., 2001; Kergoat et al., 2002; Iseri et al., 2006; Berisha et al., 2007; Paquet et al., 2007; Lu et al., 2010; Kesler et al., 2011; Kirbas et al., 2013; Kromer et al., 2013; Marziani et al., 2013; Moreno-Ramos et al., 2013; Ascaso et al., 2014; Garcia-Martin et al., 2014; Gharbiya et al., 2014; Larrosa et al., 2014; Polo et al., 2014; Bambo et al., 2015; Cheung et al., 2015; Gao et al., 2015; Güneş et al., 2015; Liu et al., 2015; Oktem et al., 2015; Cunha et al., 2016; Eraslan et al., 2016; Garcia-Martin et al., 2016; La Morgia et al., 2016; Pillai et al., 2016; Cunha et al., 2017; Ferrari et al., 2017; Golzan et al., 2017; Kwon et al., 2017; Polo et al., 2017; Trebbastoni et al., 2017; Santos et al., 2018; den Haan et al., 2019b; Hadoux et al., 2019; Kim and Kang, 2019; Querques et al., 2019; Zabel et al., 2019a, b, 2021; Zhang et al., 2019b; Asanad et al., 2020; Boqoaied et al., 2020; Criscuolo et al., 2020; Ito et al., 2020; Jindahra et al., 2020; Lemmens et al., 2020; Marquié et al., 2020; Mavilio et al., 2020; Carazo-Barrios et al., 2021; Zhao et al., 2021) to compare retinal nerve fibre layers between patients with AD and controls found statistically significant results with thinning of various retinal layers in AD patients compared to controls. Whilst most retinal thinning was noted and pronounced in the overall mean RNFL (mRNFL), The GCIPL thickness also showed significant thinning between AD and HC. Sectorally, the thinning effects were most pronounced in the superior regions. We also found, pRNFL (peripapillary RNFL) thickness to be consistently lower in the superior and inferior quadrant whereas the nasal and temporal quadrant showed mixed results in a few studies, but overall, these were thinner in AD patients. It should also be noted that a few studies have shown no significant difference in retinal thickness between AD patients and controls (Gharbiya et al., 2014; Pillai et al., 2016; den Haan et al., 2018, 2019b; Jiang et al., 2018a). However, a large cohort study has reported that RNFL thinning was associated with an increased risk of dementia due to AD (Mutlu et al., 2018).
The retina being an extension of the central nervous system and sharing a common embryological origin may reflect cortical atrophy changes as a nonspecific marker for neurodegeneration. The work by Den Haan et al. (2017b) further supports this study as they found a significant association between OCT findings and the degree of cortical atrophy in patients with AD. Overall whilst retinal changes are being studied extensively in AD, retinal thinning is not AD-specific and is observed in other neurodegenerative diseases (Satue et al., 2014; Yap et al., 2019). Similar findings have been observed in Parkinson’s disease and Lewy body dementia (Moreno-Ramos et al., 2013) once again broadening the scope for the use of this technology in further areas.
Twenty-seven studies included a component of macular measurements comprising either macular thickness, macular volume, or a combination of both (Iseri et al., 2006; Moschos et al., 2012; Ascaso et al., 2014; Polo et al., 2014; Cheung et al., 2015; Gao et al., 2015; Liu et al., 2015; Salobrar-Garcia et al., 2015; Bulut et al., 2016; Cunha et al., 2016; Giménez Castejón et al., 2016; Pillai et al., 2016; Ferrari et al., 2017; Kwon et al., 2017; Polo et al., 2017; Jiang et al., 2018b; Uchida et al., 2018; den Haan et al., 2019b; Kim and Kang, 2019; Nunes et al., 2019; Querques et al., 2019; Zabel et al., 2019b; Asanad et al., 2020; Ito et al., 2020; Sánchez et al., 2020; Sen et al., 2020). Whilst the majority of studies reported that patients with AD tend to have reduced macular volume, foveal thickness, and GCIPL, three studies (Pillai et al., 2016; Polo et al., 2017; Uchida et al., 2018) showed control patients having thicker parameters. with one study reporting macular layers were thinner in patients with mild AD and thicker in those with moderate AD compared with controls (Salobrar-Garcia et al., 2019). Likely due to mild AD pathology being reflected mostly in the central macular, disease progression reflects thinning of the peripapillary retina and thickening of the central retina in contrast. Since other age-related pathologies such as epiretinal membranes and vitreomacular traction can cause macular thickening, it is important that these co-morbidities be excluded from any analysis as their presence may explain some discrepancies in results.
The measurement of RNFL thickness provides an additional technique to assist in the early diagnosis of various neurodegenerative diseases but remains non-specific. Though many studies have previously looked at the RNFL thickness and have reported a decrease in overall RNFL thickness between AD and control cohorts, retinal imaging remains limited in its clinical use as a screening tool.
The addition of OCTA to observe concurrent vascular changes could serve as an additional supporting measure (Akil et al., 2017). Peripapillary OCTA has been a growing area of research in AD. The radial peripapillary capillaries are a distinctive vascular network within the RNFL around the optic disc that has fewer anastomoses when compared with the SCP, which may make the vessels more susceptible to vascular dysfunction making them an attractive target for the study of AD (Akil et al., 2017; Wang et al., 2018; Saks et al., 2022; Shen et al., 2022). Nevertheless, the limited number of studies that have been performed in this area have found mixed results (Bulut et al., 2018; Lahme et al., 2018; Wang et al., 2018; den Haan et al., 2019a; Querques et al., 2019; Sadda et al., 2019; Yoon et al., 2019a; Zabel et al., 2019b; Zhang et al., 2019a; Chua et al., 2021; Yan et al., 2021). Lahme et al. (2018) reported a reduction of peripapillary vessel density in patients with clinical AD. Conversely, two other studies found no significant differences in the peripapillary vascularity between patients with AD and HC subjects (den Haan et al., 2019a; Yoon et al., 2019a). Zhang et al. (2019a) did not detect any statistically significant difference in peripapillary parameters between patients with amnestic MCI and HC. The findings of this analysis were that there was generally a reduced SVD and DVD recorded in AD compared to controls. The FAZ however revealed conflicting results. Previous studies have reported an enlarged FAZ in subjects with AD compared to controls (Bulut et al., 2018; Zabel et al., 2019b); however, this analysis found on average a slightly larger FAZ in HC which does not support a vascular deficit at the macula in AD. This could be attributed to the limited sampling power of included studies for assessing this metric or likely due to the heterogeneity or variation that exists in the FAZ region and techniques used for its measurement (Fujiwara et al., 2017).
One of the most important features to note in this analysis is the variation that exists in measuring VD among different machines. OCTA VD metrics vary greatly among machines with a single machine capable of utilizing various metrics to measure VD. We addressed this concern by once again using the SMD as a summary metric and pooling all measures that included VD. It should be mentioned that in studies conducted by Querques et al. (2019) and Yoon et al. (2019b), two different scan depths were measured for the SVD (3 mm × 3 mm and 6 mm × 6 mm). The development and integration of OCTA technology in addition to neuroimaging and cognitive testing lend additional support to the available imaging studies.
Limitations
This meta-analysis was conducted to answer the question ‘Is there an association between OCT and OCTA measurements within the retina in controls and AD’. Despite including studies with a high quality that addressed this research question, the results of this analysis should be interpreted in the context of several potential limitations. Firstly, the study heterogeneity that existed among all included studies was often attributable to different OCT/OCTA instrumentation, the selection of eyes for analysis, the thickness of the retina measured, and the parameters examined may have influenced the pooled estimates produced. Of note, we acknowledge that differences exist in the thickness of patient retina which may vary both before and after the onset of AD. This difference although well-known was unable to be standardized as the majority of patients reported in these studies were seen cross-sectionally and not as part of a longitudinal follow-up study. As we have gathered and analyzed data from several published studies, we acknowledge differences in testing methods of clinicians/researchers as well as instrumentation such as OCT/OCTA including brand and type. Although most included studies matched cases of AD and MCI with controls based on age, sex, or a combination of both, there is still variation between study criteria. Secondly, the cognitive status of patients classified as AD varied amongst the included studies, the mean Mini-Mental State Examination scores for the analysis ranged from 16.4 to 29.2. An over or underestimation may lead to variability in disease classification and either an over or underestimation of the true effect. While care was taken only to include studies where another pathology was excluded in both AD and control subjects in the entry criteria, it is possible that confounding ocular co-morbidities such as glaucoma, high myopia, macular degeneration, epiretinal membranes, and systemic conditions such as diabetes and hypertension may not have been adequately excluded in some cases. In this study, we utilized and based our decision of an AD diagnosis according to established diagnostic systems (e.g., DSM-III, DSM-IV, NINDS-ADRDA, Petersen Criteria), we acknowledge the existence of multiple AD types (limbic, inflammatory, familial, and onset dependent) however for this study we did not factor these subtypes into the analysis based on data availability.
The strengths of our meta-analysis are the inclusion of many AD and control patient cohorts, as well as the inclusion of various parameters that could serve as potential biomarkers for AD diagnosis. Secondly to the best of our knowledge, this is one of the largest meta-analyses that has evaluated sectoral RNFL thickness between AD and control subjects providing valuable contributions to a very relevant field. The study highlights the potential clinical utility of non-invasive retinal imaging examinations such as those parameters discussed in the analysis as valuable markers for an insight into the aging brain and the prediction of cognitive decline. Future studies that arise in the field may aim to explore and answer how AD severity may correlate with retinal changes for which multiple well-constructed longitudinal cohort studies are needed. In the current meta-analysis, we concluded that global and sectoral RNFL thickness and GCIPL were decreased in patients with AD.
Conclusions
Overall macular thickness parameters were also reduced in AD patients. Vascular density measures on OCTA were also reduced in AD. Consequently, OCT/OCTA may have the potential to detect retinal changes and microvascular deficits in patients with AD and aid in the techniques currently available to diagnose and manage patients with AD.
Author contributions:SS, TS and VG conceived and designed the study. SS, TS, and SA searched the literature. SS, NC and VG contributed to the writing of the original manuscript. SS, TS and SA contributed to data acquisition and analysis. SS and DS were responsible for software analysis. MM, VG, YY, AS and SLG were responsible for project and study management. All authors approved the final version of the manuscript.
Conflicts of interest:The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Data availability statement:The original contributions presented in the study are included in the article and its Additional files, and further inquiries can be directed to the corresponding author.
Additional files:
Additional file 1: Study design-specific critical appraisal tools.
Additional file 2: Critical appraisal of the included studies.
C-Editors: Zhao M, Liu WJ; S-Editor: Li CH; L-Editors: Li CH, Song LP; T-Editor: Jia Y
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