Dataset Information

Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients.

ABSTRACT: Diet assessment tools provide valuable nutrition information in research and clinical settings. With growing evidence supporting dietary modification to delay development and progression of age-related macular degeneration (AMD), an AMD-specific diet assessment tool could encourage eye-care practitioners to refer patients in need of further dietary behavioural support to a dietitian and/or support network. Therefore, the aim of this study was to evaluate clinical use of a novel, short dietary questionnaire (SDQ-AMD) to screen for inadequate food intake in AMD patients by comparing it against a validated food frequency questionnaire (FFQ). Recruitment sources included Sydney-based private eye clinics and research databases (N = 155; 57% female; 78 ± 8 years). Scoring criteria based on the Australian Dietary Guidelines and dietary recommendations for AMD in literature were developed and applied to dietary data from the FFQ and SDQ-AMD. Bland-Altman plot of difference suggests agreement between the FFQ and SDQ-AMD as most mean difference scores were within the 95% CI (6.91, -9.94), and no significant bias between the scores as the mean score increased ((regression equation: y = 0.11x - 2.60) (95% CI: -0.058, 0.275, p-value = 0.20)). Scores were also significantly correlated (0.57, p ? 0.0001). The SDQ-AMD shows potential as a diet screening tool for clinical use, however, additional studies are warranted to validate the SDQ-AMD.

SUBMITTER: Tang D

PROVIDER: S-EPMC6949902 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients.

Tang Diana D Mitchell Paul P Liew Gerald G Burlutsky George G Flood Victoria V Gopinath Bamini B

Nutrients 20191212 12

Diet assessment tools provide valuable nutrition information in research and clinical settings. With growing evidence supporting dietary modification to delay development and progression of age-related macular degeneration (AMD), an AMD-specific diet assessment tool could encourage eye-care practitioners to refer patients in need of further dietary behavioural support to a dietitian and/or support network. Therefore, the aim of this study was to evaluate clinical use of a novel, short dietary qu ...[more]

PMID: 31842257

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Project description:ObjectiveAge-related macular degeneration (AMD) is a significant cause of blindness, initially characterized by the accumulation of sub-Retinal pigment epithelium (RPE) deposits, leading to progressive retinal degeneration and, eventually, irreversible vision loss. This study aimed to elucidate the differential expression of transcriptomic information in AMD and normal human RPE choroidal donor eyes and to investigate whether it could be used as a biomarker for AMD.MethodsRPE choroidal tissue samples (46 Normal samples, 38 AMD samples) were obtained from the GEO (GSE29801) database and screened for differentially expressed genes in normal and AMD patients using GEO2R and R to compare the degree of enrichment of differentially expressed genes in the GO, KEGG pathway. Firstly, we used machine learning models (LASSO, SVM algorithm) to screen disease signature genes and compare the differences between these signature genes in GSVA and immune cell infiltration. Secondly, we also performed a cluster analysis to classify AMD patients. We selected the best classification by weighted gene co-expression network analysis (WGCNA) to screen the key modules and modular genes with the strongest association with AMD. Based on the module genes, four machine models, RF, SVM, XGB, and GLM, were constructed to screen the predictive genes and further construct the AMD clinical prediction model. The accuracy of the column line graphs was evaluated using decision and calibration curves.ResultsFirstly, we identified 15 disease signature genes by lasso and SVM algorithms, which were associated with abnormal glucose metabolism and immune cell infiltration. Secondly, we identified 52 modular signature genes by WGCNA analysis. We found that SVM was the optimal machine learning model for AMD and constructed a clinical prediction model for AMD consisting of 5 predictive genes.ConclusionWe constructed a disease signature genome model and an AMD clinical prediction model by LASSO, WGCNA, and four machine models. The disease signature genes are of great reference significance for AMD etiology research. At the same time, the AMD clinical prediction model provides a reference for early clinical detection of AMD and even becomes a future census tool. In conclusion, our discovery of disease signature genes and AMD clinical prediction models may become promising new targets for the targeted treatment of AMD.

Dataset Information

Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients.

Publications

Evaluation of a Novel Tool for Screening Inadequate Food Intake in Age-Related Macular Degeneration Patients.

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