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Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008

Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evalu...

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Autores principales: Blighe, Kevin, Gurudas, Sarega, Lee, Ying, Sivaprasad, Sobha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696981/
https://www.ncbi.nlm.nih.gov/pubmed/33198349
http://dx.doi.org/10.3390/jcm9113643
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author Blighe, Kevin
Gurudas, Sarega
Lee, Ying
Sivaprasad, Sobha
author_facet Blighe, Kevin
Gurudas, Sarega
Lee, Ying
Sivaprasad, Sobha
author_sort Blighe, Kevin
collection PubMed
description Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR compared to the established risk factors. Methods: we performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007–2008 in individuals with diabetes with DR as the outcome (test set). We employed independent variable (feature) selection approaches, including parallelised univariate regression modelling, Principal Component Analysis (PCA), penalised regression, and RandomForest™. These models were replicated in NHANES 2005–2006 (replication set). Our test and replication sets consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had an Area under the Curve (AUC) 0.796 (95% CI 0.761–0.832), while penalised regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72–0.77). RandomForest™ identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65–0.77). On grouping the selected variables in our RandomForest™, hyperglycaemia alone achieved AUC 0.72 (95% CI 0.68–0.76). The AUC increased to 0.84 (95% CI 0.78–0.9) when the model also included hypertension, hypercholesterolemia, haematocrit, renal, and liver function tests.
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spelling pubmed-76969812020-11-29 Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008 Blighe, Kevin Gurudas, Sarega Lee, Ying Sivaprasad, Sobha J Clin Med Article Several circulating biomarkers are reported to be associated with diabetic retinopathy (DR). However, their relative contributions to DR compared to known risk factors, such as hyperglycaemia, hypertension, and hyperlipidaemia, remain unclear. In this data driven study, we used novel models to evaluate the associations of over 400 laboratory parameters with DR compared to the established risk factors. Methods: we performed an environment-wide association study (EWAS) of laboratory parameters available in National Health and Nutrition Examination Survey (NHANES) 2007–2008 in individuals with diabetes with DR as the outcome (test set). We employed independent variable (feature) selection approaches, including parallelised univariate regression modelling, Principal Component Analysis (PCA), penalised regression, and RandomForest™. These models were replicated in NHANES 2005–2006 (replication set). Our test and replication sets consisted of 1025 and 637 individuals with available DR status and laboratory data respectively. Glycohemoglobin (HbA1c) was the strongest risk factor for DR. Our PCA-based approach produced a model that incorporated 18 principal components (PCs) that had an Area under the Curve (AUC) 0.796 (95% CI 0.761–0.832), while penalised regression identified a 9-feature model with 78.51% accuracy and AUC 0.74 (95% CI 0.72–0.77). RandomForest™ identified a 31-feature model with 78.4% accuracy and AUC 0.71 (95% CI 0.65–0.77). On grouping the selected variables in our RandomForest™, hyperglycaemia alone achieved AUC 0.72 (95% CI 0.68–0.76). The AUC increased to 0.84 (95% CI 0.78–0.9) when the model also included hypertension, hypercholesterolemia, haematocrit, renal, and liver function tests. MDPI 2020-11-12 /pmc/articles/PMC7696981/ /pubmed/33198349 http://dx.doi.org/10.3390/jcm9113643 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Blighe, Kevin
Gurudas, Sarega
Lee, Ying
Sivaprasad, Sobha
Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title_full Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title_fullStr Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title_full_unstemmed Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title_short Diabetic Retinopathy Environment-Wide Association Study (EWAS) in NHANES 2005–2008
title_sort diabetic retinopathy environment-wide association study (ewas) in nhanes 2005–2008
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7696981/
https://www.ncbi.nlm.nih.gov/pubmed/33198349
http://dx.doi.org/10.3390/jcm9113643
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