MacuTest Website for Personalized AMD Risk Prediction and Prevention

NCT ID: NCT05675917

Last Updated: 2024-02-12

Basic Information

• Recruitment Status: WITHDRAWN
• Clinical Phase: NA
• Study Classification: INTERVENTIONAL
• Study Start Date: 2024-02-02
• Study Completion Date: 2024-02-02

Brief Summary

LEHA team of Bordeaux University has developed an Age-related Macular Degeneration (AMD) prediction algorithm (taking into account age, 49 genetic variants, the presence of early retinal abnormalities, tobacco consumption, food quality, blood pressure and education level) and is currently developing the MacuTest website, integrating this prediction algorithm. This platform offers participants the possibility to enter their personal lifestyle data, to couple them with an ophthalmological examination and a genetic test, in order to evaluate their personalized risk of AMD. The main objective of this pilot study is to evaluate the feasibility of estimating the predicted AMD risk

Detailed Description

AMD is the leading cause of blindness in industrialized countries. Current treatments only address the neovascular form and do not always prevent vision loss. This multifactorial pathology involves both genetic factors (more than 50 loci identified) and environmental factors. The genes identified suggest an important contribution of inflammation and innate immunity as well as lipid metabolism in the physiopathology of AMD. As a potential target for preventive actions, the role of lifestyle has also been the subject of much work. Thus, epidemiological studies have identified smoking as an important risk factor. The role of nutrition and metabolism in ocular aging is of growing interest, with hypotheses focusing more specifically on the joint effect of antioxidants and lipids. It is therefore important to develop prevention strategies. In this context, it is necessary to be able to identify at an early stage those people most at risk of developing AMD, in order to propose interventions aimed at reducing the risk of visual loss (reinforced ophthalmological follow-up for rapid detection and treatment of neovascular forms, lifestyle recommendations, nutritional supplementation, etc.).

During the baseline (V1) and the 12-months follow-up (V3) visits, lifestyle data (tobacco, diet, education) will be collected within online self-questionnaires on the MacuTest platform. Ophthalmological examination with evaluation of retinal anomalies, blood pressure, and saliva collection (inclusion only) for genetic testing will be perform by the ophthalmologist.

At the 6-months follow-up visit (V2), the ophthalmologist will give the participant the results of the genetic test and explanations and interpretation of the results he or she deems necessary.

At the 12-months follow-up visit (V3), the participant will fill in a questionnaire to evaluate the MacuTest platform.

Conditions

Macular Degeneration

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

MacuTest website

The MacuTest platform will be used to collect the data needed to predict AMD risk. AMD risk prediction is evaluated by a mathematical algorithm based on the data collected and integrated into the platform.

The ophthalmologist will fill in the ophthalmologist questionnaire in the platform with data on the fundus and blood pressure, after examining the patient.

The patient will fill in the lifestyle questionnaires (nutrition, smoking, gender, year of birth, level of education) with his personal equipment (computer, smartphone or tablet).

Group Type: EXPERIMENTAL

MacuTest platform

Intervention Type: DEVICE

The MacuTest platform will be used to collect the data needed to predict AMD risk. AMD risk prediction is evaluated by a mathematical algorithm based on the data collected and integrated into the platform.

The ophthalmologist will fill in the ophthalmologist questionnaire in the platform with data on the fundus and blood pressure, after examining the patient.

The patient will fill in the lifestyle questionnaires (nutrition, smoking, gender, year of birth, level of education) with his personal equipment (computer, smartphone or tablet).

Interventions

MacuTest platform

The MacuTest platform will be used to collect the data needed to predict AMD risk. AMD risk prediction is evaluated by a mathematical algorithm based on the data collected and integrated into the platform.

The ophthalmologist will fill in the ophthalmologist questionnaire in the platform with data on the fundus and blood pressure, after examining the patient.

The patient will fill in the lifestyle questionnaires (nutrition, smoking, gender, year of birth, level of education) with his personal equipment (computer, smartphone or tablet).

Intervention Type: DEVICE

Eligibility Criteria

Inclusion Criteria

• Patients aged 65 years and older of European geographic origin
• Presence of early signs of AMD (drusen and/or pigment abnormalities) and/or parents with advanced AMD.
• Absence of any advanced form of AMD (atrophic or neovascular AMD).
• Patient who can read, understand and speak French.
• Patient with access to the MacuTest internet platform throughout the study (telephone, tablet, computer).
• Patient with the ability and willingness to carry out all planned visits and assessments.
• Patient with health insurance.
• Signed informed consent.

Exclusion Criteria

• Presence of a severe ocular pathology that prevents an examination of the fundus.
• Patient with a history of a medical condition that, in the opinion of the investigator, would prevent the completion of scheduled study visits and completion of the study.
• Adult under legal protection or residing in a health or social institution.

• Minimum Eligible Age: 65 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

University of Bordeaux

OTHER

Sponsor Role: collaborator

University Hospital, Bordeaux

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Other Identifiers

CHUBX 2022/46

Identifier Type: -

Identifier Source: org_study_id