IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0157148.html
   My bibliography  Save this article

Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations

Author

Listed:
  • Kevin V Lemley
  • Serena M Bagnasco
  • Cynthia C Nast
  • Laura Barisoni
  • Catherine M Conway
  • Stephen M Hewitt
  • Peter X K Song

Abstract

Objective: Most predictive models of kidney disease progression have not incorporated structural data. If structural variables have been used in models, they have generally been only semi-quantitative. Methods: We examined the predictive utility of quantitative structural parameters measured on the digital images of baseline kidney biopsies from the NEPTUNE study of primary proteinuric glomerulopathies. These variables were included in longitudinal statistical models predicting the change in estimated glomerular filtration rate (eGFR) over up to 55 months of follow-up. Results: The participants were fifty-six pediatric and adult subjects from the NEPTUNE longitudinal cohort study who had measurements made on their digital biopsy images; 25% were African-American, 70% were male and 39% were children; 25 had focal segmental glomerular sclerosis, 19 had minimal change disease, and 12 had membranous nephropathy. We considered four different sets of candidate predictors, each including four quantitative structural variables (for example, mean glomerular tuft area, cortical density of patent glomeruli and two of the principal components from the correlation matrix of six fractional cortical areas–interstitium, atrophic tubule, intact tubule, blood vessel, sclerotic glomerulus, and patent glomerulus) along with 13 potentially confounding demographic and clinical variables (such as race, age, diagnosis, and baseline eGFR, quantitative proteinuria and BMI). We used longitudinal linear models based on these 17 variables to predict the change in eGFR over up to 55 months. All 4 models had a leave-one-out cross-validated R2 of about 62%. Conclusions: Several combinations of quantitative structural variables were significantly and strongly associated with changes in eGFR. The structural variables were generally stronger than any of the confounding variables, other than baseline eGFR. Our findings suggest that quantitative assessment of diagnostic renal biopsies may play a role in estimating the baseline risk of succeeding loss of renal function in future clinical studies, and possibly in clinical practice.

Suggested Citation

  • Kevin V Lemley & Serena M Bagnasco & Cynthia C Nast & Laura Barisoni & Catherine M Conway & Stephen M Hewitt & Peter X K Song, 2016. "Morphometry Predicts Early GFR Change in Primary Proteinuric Glomerulopathies: A Longitudinal Cohort Study Using Generalized Estimating Equations," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-13, June.
  • Handle: RePEc:plo:pone00:0157148
    DOI: 10.1371/journal.pone.0157148
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0157148
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0157148&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0157148?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Wei Pan, 2001. "Akaike's Information Criterion in Generalized Estimating Equations," Biometrics, The International Biometric Society, vol. 57(1), pages 120-125, March.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Heidi Seibold & Severin Czerny & Siona Decke & Roman Dieterle & Thomas Eder & Steffen Fohr & Nico Hahn & Rabea Hartmann & Christoph Heindl & Philipp Kopper & Dario Lepke & Verena Loidl & Maximilian Ma, 2021. "A computational reproducibility study of PLOS ONE articles featuring longitudinal data analyses," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.

    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. Wei Pan, 2001. "Model Selection in Estimating Equations," Biometrics, The International Biometric Society, vol. 57(2), pages 529-534, June.
    2. Vens, Maren & Ziegler, Andreas, 2012. "Generalized estimating equations and regression diagnostics for longitudinal controlled clinical trials: A case study," Computational Statistics & Data Analysis, Elsevier, vol. 56(5), pages 1232-1242.
    3. Michael S. Rendall & Bonnie Ghosh-Dastidar & Margaret M. Weden & Zafar Nazarov, 2011. "Multiple Imputation for Combined-Survey Estimation With Incomplete Regressors In One But Not Both Surveys," Working Papers WR-887-1, RAND Corporation.
    4. Katrina N. Burns & Kan Sun & Julius N. Fobil & Richard L. Neitzel, 2016. "Heart Rate, Stress, and Occupational Noise Exposure among Electronic Waste Recycling Workers," IJERPH, MDPI, vol. 13(1), pages 1-16, January.
    5. Song Guo & Feng Ling & Juan Hou & Jinna Wang & Guiming Fu & Zhenyu Gong, 2014. "Mosquito Surveillance Revealed Lagged Effects of Mosquito Abundance on Mosquito-Borne Disease Transmission: A Retrospective Study in Zhejiang, China," PLOS ONE, Public Library of Science, vol. 9(11), pages 1-8, November.
    6. Marc-Andreas Muendler & Sascha O. Becker, 2010. "Margins of Multinational Labor Substitution," American Economic Review, American Economic Association, vol. 100(5), pages 1999-2030, December.
    7. Laura Neumeyer & Anna Gründler & Anna-Luisa Stöber, 2023. "Don’t Worry, Be Happy—Does the CEO’s Personality Mitigate the Negative Effect of Financial Constraints on Employee Satisfaction?," Schmalenbach Journal of Business Research, Springer, vol. 75(1), pages 71-98, March.
    8. Amy Hinsley & William J Sutherland & Alison Johnston, 2017. "Men ask more questions than women at a scientific conference," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-14, October.
    9. Geraldo F Oliveira & Teresinha R R Oliveira & Adauto T Ikejiri & Mariela P Andraus & Tais F Galvao & Marcus T Silva & Maurício G Pereira, 2014. "Prevalence of Hypertension and Associated Factors in an Indigenous Community of Central Brazil: A Population-Based Study," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-6, January.
    10. Li, Gaorong & Lian, Heng & Feng, Sanying & Zhu, Lixing, 2013. "Automatic variable selection for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 174-186.
    11. Beth A. Reboussin & Edward H. Ip & Mark Wolfson, 2008. "Locally dependent latent class models with covariates: an application to under‐age drinking in the USA," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(4), pages 877-897, October.
    12. Peng, Cheng & Yang, Yihe & Zhou, Jie & Pan, Jianxin, 2022. "Latent Gaussian copula models for longitudinal binary data," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    13. Mark Rooij, 2018. "Transitional modeling of experimental longitudinal data with missing values," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(1), pages 107-130, March.
    14. Tessa Recendes & Federico Aime & Aaron D. Hill & Oleg V. Petrenko, 2022. "Bargaining your way to success: The effect of Machiavellian chief executive officers on firm costs," Strategic Management Journal, Wiley Blackwell, vol. 43(10), pages 2012-2041, October.
    15. Bastian Rake, 2017. "Determinants of pharmaceutical innovation: the role of technological opportunities revisited," Journal of Evolutionary Economics, Springer, vol. 27(4), pages 691-727, September.
    16. Muhammad Adeel & Hung-Chou Chen & Bor-Shing Lin & Chien-Hung Lai & Chun-Wei Wu & Jiunn-Horng Kang & Jian-Chiun Liou & Chih-Wei Peng, 2022. "Oxygen Consumption (VO 2 ) and Surface Electromyography (sEMG) during Moderate-Strength Training Exercises," IJERPH, MDPI, vol. 19(4), pages 1-14, February.
    17. Aristides dos Santos, Anderson Moreira & Perelman, Julian & Jacinto, Paulo de Andrade & Tejada, Cesar Augusto Oviedo & Barros, Aluísio J.D. & Bertoldi, Andréa D. & Matijasevich, Alicia & Santos, Iná S, 2019. "Income-related inequality and inequity in children’s health care: A longitudinal analysis using data from Brazil," Social Science & Medicine, Elsevier, vol. 224(C), pages 127-137.
    18. Fischer, Caroline & Schott, Carina, 2020. "Why People Enter and Stay in Public Service Careers: The Role of Parental Socialization and an Interest in Politics," OSF Preprints yb8e3, Center for Open Science.
    19. Yuvraj Sunecher & Naushad Mamode Khan & Miroslav M. Ristić & Vandna Jowaheer, 2019. "BINAR(1) negative binomial model for bivariate non-stationary time series with different over-dispersion indices," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 625-653, December.
    20. Irina Chis Ster & Hamzah F Niaz & Martha E Chico & Yisela Oviedo & Maritza Vaca & Philip J Cooper, 2021. "The epidemiology of soil-transmitted helminth infections in children up to 8 years of age: Findings from an Ecuadorian birth cohort," PLOS Neglected Tropical Diseases, Public Library of Science, vol. 15(11), pages 1-20, November.

    More about this item

    Statistics

    Access and download statistics

    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:plo:pone00:0157148. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.