Application of specific macular morphological characteristics as biomarker for early prediction of age-related macular degeneration

Abstract

The invention discloses application of specific macular morphological characteristics as a biomarker for early prediction of age-related macular degeneration. The specific macular morphological characteristics are as follows: under non-mydriasis natural light, the macular is divided into a central area, an inner ring and an outer ring, the inner ring and the outer ring are further divided into four directions of upper and lower nasal temple, and the specific macular morphological characteristics are shown on the upper parts of the thin inner ring and the outer ring, the thick central area and other outer ring parts. The invention reveals that specific macular morphological characteristics can be used as a biomarker for early prediction of age-related macular degeneration through a potential profile analysis method for the first time. According to comparison with a population macular thickness reference value, the high-risk population of senile macular degeneration is identified through the deviation value, and risk stratification and early intervention are promoted.

Description

Technical Field

[0001] This invention relates to the field of biomedicine, specifically to the application of specific macular morphological characteristics as biomarkers for early prediction of age-related macular degeneration. Background Technology

[0002] Age-related macular degeneration (AMD) is a leading cause of vision impairment in middle-aged and older adults. Because the macula is crucial for central vision, color perception, detail recognition, and overall visual acuity, AMD significantly impacts daily life. With the aging global population and increasing life expectancy, the global prevalence of AMD is projected to rise significantly to 288 million people by 2040.

[0003] Early-stage age-related macular degeneration (AMD) often presents as a dry form, characterized by the accumulation of hard exudates, while later stages may develop into a wet form with neovascularization. Diagnosis and monitoring of AMD are typically performed using various methods, including fundus photography, optical coherence tomography (OCT), OCT angiography, microfield measurement, Amsler grid test, and fundus autofluorescence imaging. Among these methods, OCT is widely used due to its convenience, high accuracy, and easily understandable indicators. Previous studies have identified various OCT features of AMD, primarily focusing on individuals already diagnosed with the disease. With increasing demand for disease prediction and early diagnosis, recent research has increasingly focused on identifying early biomarkers that can predict the onset of AMD. Previous studies have found that susceptible individuals exhibit thickening of the retinal pigment epithelium-Bruch's membrane complex and thinning of photoreceptors, providing crucial insights for early prediction of AMD. However, data derived from specific optical coherence tomography (OCT) layers are frequently affected by segmentation errors, physiological variability, and nonspecific changes, which may reduce their robustness in clinical prediction. Furthermore, long-term monitoring of changes in specific retinal layers on OCT scans is not feasible in public health practice for individuals not yet diagnosed with age-related macular degeneration (AMD). In contrast, macular morphology provides a more readily available and comprehensive perspective; however, its predictive value for AMD has not been fully investigated. Summary of the Invention

[0004] The purpose of this invention is to provide the application of specific macular morphological features as biomarkers for early prediction of age-related macular degeneration.

[0005] The specific macular morphological characteristics mentioned are:

[0006] Under non-mydriatic natural light, the macula is divided into a central area, an inner ring, and an outer ring. The inner and outer rings are further divided into four areas: upper, lower, nasotemporal, and lateral, for a total of nine areas. Among them, the macula exhibits a thin inner ring and the upper part of the outer ring, a thick central area, and other parts of the outer ring, which are specific morphological characteristics of the macula.

[0007] The present invention also provides the application of the device for detecting the above-mentioned specific macular morphological characteristics in the preparation of a device for early prediction of age-related macular degeneration.

[0008] The present invention also provides a device for diagnosing early prediction of age-related macular degeneration, comprising:

[0009] Collection module: Under non-mydriatic natural light, the macula is divided into the central area, inner ring and outer ring. The inner and outer rings are further divided into the superior, inferior and nasotemporal directions, for a total of 9 areas. The macular information of the patient in 9 areas is collected.

[0010] Determination module: If the macula shows a thin inner ring and upper outer ring, and a thick central area and other outer ring parts, then it is a high-risk group for age-related macular degeneration.

[0011] This invention, for the first time, reveals through potential profile analysis that specific macular morphological features can serve as biomarkers for early prediction of age-related macular degeneration (AMD). By comparing these features with population-referenced macular thickness values, deviations can be used to identify high-risk individuals for AMD, promoting risk stratification and early intervention. Attached Figure Description

[0012] Figure 1 This is a distribution map of nine regions. Detailed Implementation

[0013] The following embodiments are further illustrations of the present invention, but not limitations thereof.

[0014] Example 1:

[0015] 1. Research Methods

[0016] Study Design: A prospective cohort study based on participants in UK biobanks

[0017] Participants: A total of 40,078 participants without age-related macular degeneration were included. The study ethics were approved by the Northwest Multicenter Research Ethics Committee (No.: 21 / NW / 0157). Participants underwent optical coherence tomography (OCT) at enrollment using a Topcon 3D OCT-1000Mk2 device under non-mydriatic natural light. Macular thickness information for nine regions was obtained for the "Early Treatment of Diabetic Retinopathy Study," including the central region, inner ring, and outer ring. The inner and outer rings are further divided into superior, inferior, nasotemporal, and quadrilateral regions. (Details are as follows...) Figure 1 As shown.

[0018] 2. Key Technologies:

[0019] Latent profiling analysis was used to identify thickness feature patterns in nine macular zones. A total of four models were selected, including:

[0020] Model a: Assumes equal variances and a fixed covariance of 0.

[0021] Model b: Assumes variable variance and fixed covariance of 0.

[0022] Model C: Assumes equal variances and equal covariances.

[0023] Model d: Assumes variable variance and variable covariance.

[0024] To ensure the comparability of thickness information across the nine zones, all data were standardized before being incorporated into the model, converted to standardized Z-values. Based on the Akaike Information Criterion (AIC), Bayesian Information Criterion (BIC), and classification entropy, the model and the number of profiles were determined. To avoid an excessively small subgroup sample ratio, the maximum number of profiles was limited to five. To ensure classification performance, the model entropy value was limited to be greater than 0.8. Based on the potential profile analysis results (Table 1), the 2-profile classification mode under model d was ultimately selected.

[0025] Table 1. Results of Potential Profile Analysis

[0026]

[0027]

[0028] 3. Key findings

[0029] Based on the macular thickness patterns of the participants, the macular regions were divided into two profiles. Compared to profile 1 (31,942 people), profile 2 (8,136 people) showed a thinner inner ring and upper outer ring, and a thicker central region and other outer ring parts (Table 2).

[0030] Table 2. Distribution of thickness Z in the nine zones of the yellow macular region between cross sections

[0031]

[0032] Based on a follow-up period of over ten years, a Cox proportional hazards model was constructed, and three regression models were tested (Model 1: no adjustment for covariates; Model 2: adjusted for age, sex, and race; Model 3: adjusted for age, sex, race, education, Townsend deprivation index, body mass index, smoking status, alcohol consumption status, healthy diet score, hypertension, diabetes, mean spherical equivalent, and polygenic risk score for age-related macular degeneration). All models calculated a significantly increased risk of developing age-related macular degeneration in profile 2 (Table 3).

[0033] Table 3. Risk ratio of age-related macular degeneration between cross-sections

[0034]

[0035] *Referencing section 1

[0036] 4. Clinical significance

[0037] This study identified specific morphological features of the macula as potential biomarkers for early identification of age-related macular degeneration (AMD). By comparing these features with reference values ​​for macular thickness in the general population, we identified high-risk individuals for AMD based on deviation values, thereby promoting risk stratification and early intervention.

[0038] 5. Research Results

[0039] For the first time, a potential profile analysis method has revealed that specific macular morphological features can serve as biomarkers for early prediction of age-related macular degeneration.

Claims

1. Application of specific macular morphological features as biomarkers for early prediction of age-related macular degeneration. The specific macular morphological characteristics mentioned are: Under non-mydriatic natural light, the macula is divided into a central area, an inner ring, and an outer ring. The inner and outer rings are further divided into four areas: upper, lower, nasotemporal, and lateral, for a total of nine areas. Among them, the macula exhibits a thin inner ring and the upper part of the outer ring, a thick central area, and other parts of the outer ring, which are specific morphological characteristics of the macula.

2. The application of the device for detecting specific macular morphological characteristics as described in claim 1 in the preparation of a device for diagnosing early prediction of age-related macular degeneration.

3. A device for diagnosing early prediction of age-related macular degeneration, characterized in that, include: Collection module: Under non-mydriatic natural light, the macula is divided into the central area, inner ring and outer ring. The inner and outer rings are further divided into the superior, inferior and nasotemporal directions, for a total of 9 areas. The macular information of the patient in 9 areas is collected. Determination module: If the macula shows a thin inner ring and upper outer ring, and a thick central area and other outer ring parts, then it is a high-risk group for age-related macular degeneration.

Images