IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v20y2023i6p5108-d1096723.html
   My bibliography  Save this article

Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy

Author

Listed:
  • Rossella Tatoli

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Luisa Lampignano

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Rossella Donghia

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Alfredo Niro

    (Eye Clinic, Hospital “SS. Annunziata”, ASL Taranto, 74100 Taranto, Italy)

  • Fabio Castellana

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Ilaria Bortone

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • Roberta Zupo

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Sarah Tirelli

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

  • Madia Lozupone

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • Francesco Panza

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • Giovanni Alessio

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • Francesco Boscia

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • Giancarlo Sborgia

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy)

  • on behalf of the Eye Clinic Research Group

    (Department of Translational Biomedicine and Neuroscience “DiBraiN”, University of Bari Aldo Moro, 70121 Bari, Italy
    Membership of the Eye Clinic Research Group is provided in the Acknowledgments.)

  • Rodolfo Sardone

    (National Institute of Gastroenterology—IRCCS “Saverio de Bellis”, Research Hospital, 70013 Bari, Italy)

Abstract

Background: Like other parts of the body, the retina and its neurovascular system are also affected by age-related changes. The rising age of populations worldwide makes it important to study the pathologies related to age and their potential risk factors, such as diet and eating habits. The aim of this study was to investigate the predictive power of food groups versus retinal features among noninstitutionalized older adults from Southern Italy using a machine learning approach. Methods: We recruited 530 subjects, with a mean age of 74 years, who were drawn from the large population of the Salus in Apulia Study. In the present cross-sectional study, eating habits were assessed with a validated food frequency questionnaire. For the visual assessment, a complete ophthalmic examination and optical coherence tomography-angiography analyses were performed. Results: The analyses identified 13 out of the 28 food groups as predictors of all our retinal variables: grains, legumes, olives-vegetable oil, fruiting vegetables, other vegetables, fruits, sweets, fish, dairy, low-fat dairy, red meat, white meat, and processed meat. Conclusions: Eating habits and food consumption may be important risk factors for age-related retinal changes. A diet that provides the optimal intake of specific nutrients with antioxidant and anti-inflammatory powers, including carotenoids and omega-3 fatty acids, could have beneficial effects.

Suggested Citation

  • Rossella Tatoli & Luisa Lampignano & Rossella Donghia & Alfredo Niro & Fabio Castellana & Ilaria Bortone & Roberta Zupo & Sarah Tirelli & Madia Lozupone & Francesco Panza & Giovanni Alessio & Francesc, 2023. "Retinal Microvasculature and Neural Changes and Dietary Patterns in an Older Population in Southern Italy," IJERPH, MDPI, vol. 20(6), pages 1-17, March.
    • Handle: RePEc:gam:jijerp:v:20:y:2023:i:6:p:5108-:d:1096723
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/20/6/5108/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/20/6/5108/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
    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. Zemin Zheng & Jie Zhang & Yang Li, 2022. "L 0 -Regularized Learning for High-Dimensional Additive Hazards Regression," INFORMS Journal on Computing, INFORMS, vol. 34(5), pages 2762-2775, September.
    2. Jung-sik Hong & Hyeongyu Yeo & Nam-Wook Cho & Taeuk Ahn, 2018. "Identification of Core Suppliers Based on E-Invoice Data Using Supervised Machine Learning," JRFM, MDPI, vol. 11(4), pages 1-13, October.
    3. Shang-Ming Zhou & Fabiola Fernandez-Gutierrez & Jonathan Kennedy & Roxanne Cooksey & Mark Atkinson & Spiros Denaxas & Stefan Siebert & William G Dixon & Terence W O’Neill & Ernest Choy & Cathie Sudlow, 2016. "Defining Disease Phenotypes in Primary Care Electronic Health Records by a Machine Learning Approach: A Case Study in Identifying Rheumatoid Arthritis," PLOS ONE, Public Library of Science, vol. 11(5), pages 1-14, May.
    4. Youngjoo Cho & Debashis Ghosh, 2021. "Quantile-Based Subgroup Identification for Randomized Clinical Trials," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(1), pages 90-128, April.
    5. Ismael Ahrazem Dfuf & José Manuel Mira McWilliams & María Camino González Fernández, 2019. "Multi-Output Conditional Inference Trees Applied to the Electricity Market: Variable Importance Analysis," Energies, MDPI, vol. 12(6), pages 1-24, March.
    6. Han, Dongxiao & Huang, Jian & Lin, Yuanyuan & Shen, Guohao, 2022. "Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors," Journal of Econometrics, Elsevier, vol. 230(2), pages 416-431.
    7. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," Insurance: Mathematics and Economics, Elsevier, vol. 101(PB), pages 140-162.
    8. Christine Porzelius & Martin Schumacher & Harald Binder, 2011. "The benefit of data-based model complexity selection via prediction error curves in time-to-event data," Computational Statistics, Springer, vol. 26(2), pages 293-302, June.
    9. Foucher Yohann & Danger Richard, 2012. "Time Dependent ROC Curves for the Estimation of True Prognostic Capacity of Microarray Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 11(6), pages 1-22, November.
    10. J. Choi & S. Ye & K. H. Eng & K. Korthauer & W. H. Bradley & J. S. Rader & C. Kendziorski, 2017. "IPI59: An Actionable Biomarker to Improve Treatment Response in Serous Ovarian Carcinoma Patients," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(1), pages 1-12, June.
    11. Peter Calhoun & Melodie J. Hallett & Xiaogang Su & Guy Cafri & Richard A. Levine & Juanjuan Fan, 2020. "Random forest with acceptance–rejection trees," Computational Statistics, Springer, vol. 35(3), pages 983-999, September.
    12. Hoora Moradian & Denis Larocque & François Bellavance, 2017. "$$L_1$$ L 1 splitting rules in survival forests," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(4), pages 671-691, October.
    13. Yiwei Fan & Gang Wang & Xiaoling Lu & Gaobin Wang, 2019. "Distributed forecasting and ant colony optimization for the bike-sharing rebalancing problem with unserved demands," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-26, December.
    14. Eiran Z Gorodeski & Emer Joyce & Benjamin T Gandesbery & Eugene H Blackstone & David O Taylor & W H Wilson Tang & Randall C Starling & Rory Hachamovitch, 2017. "Discordance between 'actual' and 'scheduled' check-in times at a heart failure clinic," PLOS ONE, Public Library of Science, vol. 12(11), pages 1-13, November.
    15. Makariou, Despoina & Barrieu, Pauline & Chen, Yining, 2021. "A random forest based approach for predicting spreads in the primary catastrophe bond market," LSE Research Online Documents on Economics 111529, London School of Economics and Political Science, LSE Library.
    16. Mao, Xiaojun & Peng, Liuhua & Wang, Zhonglei, 2022. "Nonparametric feature selection by random forests and deep neural networks," Computational Statistics & Data Analysis, Elsevier, vol. 170(C).
    17. Julia Gilhodes & Florence Dalenc & Jocelyn Gal & Christophe Zemmour & Eve Leconte & Jean Marie Boher & Thomas Filleron, 2020. "Comparison of Variable Selection Methods for Time-to-Event Data in High-Dimensional Settings," Post-Print hal-02934793, HAL.
    18. Demir Djekic & Erika Fagman & Oskar Angerås & George Lappas & Kjell Torén & Göran Bergström & Annika Rosengren, 2020. "Social Support and Subclinical Coronary Artery Disease in Middle-Aged Men and Women: Findings from the Pilot of Swedish CArdioPulmonary bioImage Study," IJERPH, MDPI, vol. 17(3), pages 1-16, January.
    19. Ran Dai & Cheng Zheng & Mei-Jie Zhang, 2023. "On High-Dimensional Covariate Adjustment for Estimating Causal Effects in Randomized Trials with Survival Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 15(1), pages 242-260, April.
    20. Xiajing Gong & Meng Hu & Jinzhong Liu & Geoffrey Kim & James Xu & Amy McKee & Todd Palmby & R. Angelo Claro & Liang Zhao, 2022. "Decoding kinase-adverse event associations for small molecule kinase inhibitors," Nature Communications, Nature, vol. 13(1), pages 1-9, December.

    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:20:y:2023:i:6:p:5108-:d:1096723. 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.