IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i7p3735-d529437.html
   My bibliography  Save this article

An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model

Author

Listed:
  • Farzaneh Boroumand

    (Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia
    Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad 9137673119, Iran)

  • Mohammad Taghi Shakeri

    (Department of Biostatistics, School of Health, Mashhad University of Medical Sciences, Mashhad 9137673119, Iran)

  • Touka Banaee

    (Department of Ophthalmology and Visual Sciences, University of Texas Medical Branch, Galveston, TX 77555, USA)

  • Hamidreza Pourreza

    (Department Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Hassan Doosti

    (Department of Mathematics and Statistics, Faculty of Science and Engineering, Macquarie University, Sydney 2109, Australia)

Abstract

(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.

Suggested Citation

  • Farzaneh Boroumand & Mohammad Taghi Shakeri & Touka Banaee & Hamidreza Pourreza & Hassan Doosti, 2021. "An Analysis of the Areas Occupied by Vessels in the Ocular Surface of Diabetic Patients: An Application of a Nonparametric Tilted Additive Model," IJERPH, MDPI, vol. 18(7), pages 1-14, April.
    • Handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3735-:d:529437
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/7/3735/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/18/7/3735/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Xerxes T. Seposo & Tran Ngoc Dang & Yasushi Honda, 2017. "How Does Ambient Air Temperature Affect Diabetes Mortality in Tropical Cities?," IJERPH, MDPI, vol. 14(4), pages 1-10, April.
    2. Carroll, Raymond J. & Delaigle, Aurore & Hall, Peter, 2011. "Testing and Estimating Shape-Constrained Nonparametric Density and Regression in the Presence of Measurement Error," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 191-202.
    3. P. Hall & B. Presnell, 1999. "Intentionally biased bootstrap methods," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 61(1), pages 143-158.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hassan Doosti & Peter Hall, 2016. "Making a non-parametric density estimator more attractive, and more accurate, by data perturbation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 78(2), pages 445-462, March.
    2. Lok, Thomas M. & Tabri, Rami V., 2021. "An improved bootstrap test for restricted stochastic dominance," Journal of Econometrics, Elsevier, vol. 224(2), pages 371-393.
    3. Eduardo L. Montoya & Wendy Meiring, 2016. "An F-type test for detecting departure from monotonicity in a functional linear model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(2), pages 322-337, June.
    4. repec:cep:stiecm:/2014/572 is not listed on IDEAS
    5. Min-Liang Chu & Chiao-Yu Shih & Tsung-Cheng Hsieh & Han-Lin Chen & Chih-Wei Lee & Jen-Che Hsieh, 2019. "Acute Myocardial Infarction Hospitalizations between Cold and Hot Seasons in an Island across Tropical and Subtropical Climate Zones—A Population-Based Study," IJERPH, MDPI, vol. 16(15), pages 1-14, August.
    6. Bravo, Francesco & Crudu, Federico, 2012. "Efficient bootstrap with weakly dependent processes," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3444-3458.
    7. Lorenzo Camponovo & Taisuke Otsu, 2015. "Robustness of Bootstrap in Instrumental Variable Regression," Econometric Reviews, Taylor & Francis Journals, vol. 34(3), pages 352-393, March.
    8. Sarya Natur & Odeya Damri & Galila Agam, 2022. "The Effect of Global Warming on Complex Disorders (Mental Disorders, Primary Hypertension, and Type 2 Diabetes)," IJERPH, MDPI, vol. 19(15), pages 1-9, July.
    9. Mihai C. Giurcanu, 2017. "Oracle M-Estimation for Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 38(3), pages 479-504, May.
    10. F Bravo, 2008. "Effcient M-estimators with auxiliary information," Discussion Papers 08/26, Department of Economics, University of York.
    11. Cristian Roner & Claudia Di Caterina & Davide Ferrari, 2021. "Exponential Tilting for Zero-inflated Interval Regression with Applications to Cyber Security Survey Data," BEMPS - Bozen Economics & Management Paper Series BEMPS85, Faculty of Economics and Management at the Free University of Bozen.
    12. Hazelton, Martin L. & Turlach, Berwin A., 2007. "Reweighted kernel density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 3057-3069, March.
    13. Xu, Ke-Li & Phillips, Peter C. B., 2011. "Tilted Nonparametric Estimation of Volatility Functions With Empirical Applications," Journal of Business & Economic Statistics, American Statistical Association, vol. 29(4), pages 518-528.
    14. Arnab Bhattacharjee, 2005. "Models of Firm Dynamics and the Hazard Rate of Exits: Reconciling Theory and Evidence using Hazard Regression Models," Econometrics 0503021, University Library of Munich, Germany.
    15. John P. Buonaccorsi & Giovanni Romeo & Magne Thoresen, 2018. "Model†based bootstrapping when correcting for measurement error with application to logistic regression," Biometrics, The International Biometric Society, vol. 74(1), pages 135-144, March.
    16. Y.F. Chan, Kenny & M.S. Lee, Stephen, 2001. "An exact iterated bootstrap algorithm for small-sample bias reduction," Computational Statistics & Data Analysis, Elsevier, vol. 36(1), pages 1-13, March.
    17. Daniel J. Henderson & Christopher F. Parmeter, 2009. "Imposing economic constraints in nonparametric regression: survey, implementation, and extension," Advances in Econometrics, in: Nonparametric Econometric Methods, pages 433-469, Emerald Group Publishing Limited.
    18. Francesco Bravo, "undated". "Higher order asymptotics and the bootstrap for empirical likelihood J tests," Discussion Papers 00/30, Department of Economics, University of York.
    19. Pang Du & Christopher F. Parmeter & Jeffrey S. Racine, 2012. "Nonparametric Kernel Regression with Multiple Predictors and Multiple Shape Constraints," Department of Economics Working Papers 2012-08, McMaster University.
    20. Wong, Heung & Liu, Feng & Chen, Min & Ip, Wai Cheung, 2009. "Empirical likelihood based diagnostics for heteroscedasticity in partial linear models," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3466-3477, July.
    21. Hall, Peter & Turlach, Berwin A., 1999. "Reducing bias in curve estimation by use of weights," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 67-86, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:7:p:3735-:d:529437. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.