An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model
(1) Background: As diabetes melllitus (DM) can affect the microvasculature, this study evaluates different clinical parameters and the vascular density of ocular surface microvasculature in diabetic patients. (2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic i...
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| Veröffentlicht in: | International journal of environmental research and public health 2021-04, Vol.18 (7), p.3735, Article 3735 |
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| description | (1) Background: As diabetes melllitus (DM) can affect the microvasculature, this study evaluates different clinical parameters and the vascular density of ocular surface microvasculature in diabetic patients. (2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic individuals aged 30-60 were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The Areas Occupied by Vessels (AOV) images of different diameters were calculated. To establish the sum of AOV of different sized vessels. We adopt a novel approach to investigate the association between clinical characteristics as the predictors and AOV as the outcome, that is Tilted Additive Model (TAM). We use a tilted nonparametric regression estimator to estimate the nonlinear effect of predictors on the outcome in the additive setting for the first time. (3) Results: The results show Age (p-value = 0.019) and Mean Arterial Pressure (MAP) have a significant linear effect on AOV (p-value = 0.034). We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. These effects need to be considered in future studies of ocular surface vessels of diabetic patients. |
| doi_str_mv | 10.3390/ijerph18073735 |
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(2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic individuals aged 30-60 were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The Areas Occupied by Vessels (AOV) images of different diameters were calculated. To establish the sum of AOV of different sized vessels. We adopt a novel approach to investigate the association between clinical characteristics as the predictors and AOV as the outcome, that is Tilted Additive Model (TAM). We use a tilted nonparametric regression estimator to estimate the nonlinear effect of predictors on the outcome in the additive setting for the first time. (3) Results: The results show Age (p-value = 0.019) and Mean Arterial Pressure (MAP) have a significant linear effect on AOV (p-value = 0.034). We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. These effects need to be considered in future studies of ocular surface vessels of diabetic patients.</description><identifier>ISSN: 1660-4601</identifier><identifier>ISSN: 1661-7827</identifier><identifier>EISSN: 1660-4601</identifier><identifier>DOI: 10.3390/ijerph18073735</identifier><identifier>PMID: 33918420</identifier><language>eng</language><publisher>BASEL: Mdpi</publisher><subject>Adult ; Algorithms ; Blood pressure ; Body mass ; Body mass index ; Body size ; Cameras ; Cornea ; Creatinine ; Cross-Sectional Studies ; Diabetes ; Diabetes Mellitus ; Diabetic retinopathy ; Environmental Sciences ; Environmental Sciences & Ecology ; Eye - blood supply ; Generalized linear models ; Glycated Hemoglobin ; Hemoglobin ; Humans ; Hypertension ; Life Sciences & Biomedicine ; Microvasculature ; Middle Aged ; Nonparametric statistics ; Patients ; Photography ; Public, Environmental & Occupational Health ; Radon ; Science & Technology ; Statistical analysis ; Statistical methods ; Urea</subject><ispartof>International journal of environmental research and public health, 2021-04, Vol.18 (7), p.3735, Article 3735</ispartof><rights>2021 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 (https://creativecommons.org/licenses/by/4.0/). 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We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. 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(2) Methods: In this cross-sectional study, red-free conjunctival photographs of diabetic individuals aged 30-60 were taken under defined conditions and analyzed using a Radon transform-based algorithm for vascular segmentation. The Areas Occupied by Vessels (AOV) images of different diameters were calculated. To establish the sum of AOV of different sized vessels. We adopt a novel approach to investigate the association between clinical characteristics as the predictors and AOV as the outcome, that is Tilted Additive Model (TAM). We use a tilted nonparametric regression estimator to estimate the nonlinear effect of predictors on the outcome in the additive setting for the first time. (3) Results: The results show Age (p-value = 0.019) and Mean Arterial Pressure (MAP) have a significant linear effect on AOV (p-value = 0.034). We also find a nonlinear association between Body Mass Index (BMI), daily Urinary Protein Excretion (UPE), Hemoglobin A1C, and Blood Urea Nitrogen (BUN) with AOV. (4) Conclusions: As many predictors do not have a linear relationship with the outcome, we conclude that the TAM will help better elucidate the effect of the different predictors. The highest level of AOV can be seen at Hemoglobin A1C of 9% and AOV increases when the daily UPE exceeds 600 mg. These effects need to be considered in future studies of ocular surface vessels of diabetic patients.</abstract><cop>BASEL</cop><pub>Mdpi</pub><pmid>33918420</pmid><doi>10.3390/ijerph18073735</doi><tpages>14</tpages><orcidid>https://orcid.org/0000-0003-2972-3969</orcidid><orcidid>https://orcid.org/0000-0002-3560-8070</orcidid><orcidid>https://orcid.org/0000-0002-0290-8122</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Adult Algorithms Blood pressure Body mass Body mass index Body size Cameras Cornea Creatinine Cross-Sectional Studies Diabetes Diabetes Mellitus Diabetic retinopathy Environmental Sciences Environmental Sciences & Ecology Eye - blood supply Generalized linear models Glycated Hemoglobin Hemoglobin Humans Hypertension Life Sciences & Biomedicine Microvasculature Middle Aged Nonparametric statistics Patients Photography Public, Environmental & Occupational Health Radon Science & Technology Statistical analysis Statistical methods Urea |
| title | An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model |
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