IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0133065.html
   My bibliography  Save this article

Serum Proenkephalin A Levels and Mortality After Long-Term Follow-Up in Patients with Type 2 Diabetes Mellitus (ZODIAC-32)

Author

Listed:
  • Kornelis J J van Hateren
  • Gijs W D Landman
  • Jarinke F H Arnold
  • Hanneke Joosten
  • Klaas H Groenier
  • Gerjan J Navis
  • Andrea Sparwasser
  • Stephan J L Bakker
  • Henk J G Bilo
  • Nanne Kleefstra

Abstract

Background: Two previous studies concluded that proenkephalin A (PENK-A) had predictive capabilities for stroke severity, recurrent myocardial infarction, heart failure and mortality in patients with stroke and myocardial infarction. Objectives: This study aimed to investigate the value of PENK-A as a biomarker for predicting mortality in patients with type 2 diabetes mellitus. Methods: Patients with type 2 diabetes mellitus were included from the prospective observational ZODIAC (Zwolle Outpatient Diabetes project Integrating Available Care) study. The present analysis incorporated two ZODIAC cohorts (1998 and 2001). Since blood was drawn for 1204 out of 1688 patients (71%), and information on relevant confounders was missing in 47 patients, the final sample comprised 1157 patients. Cox proportional hazard models were used for evaluating the relationship between PENK-A and (cardiovascular) mortality. Risk prediction capabilities were assessed with Harrell’s C statistics and the integrated discrimination improvement (IDI). Results: After a follow-up period of 14 years, 525 (45%) out of 1157 patients had died, of which 224 (43%) were attributable to cardiovascular factors. Higher Log PENK-A levels were not independently associated with increased (cardiovascular) mortality. Patients with PENK-A values in the highest tertile had a 49% (95%CI 1%-121%) higher risk of cardiovascular mortality compared to patients in the reference category (lowest tertile). C-values were not different after removing PENK-A from the Cox models and there were no significant differences in IDI values. Conclusions: The associations between PENK-A and mortality were strongly attenuated after accounting for all traditional risk factors. Furthermore, PENK-A did not seem to have additional value beyond conventional risk factors when predicting all-cause and cardiovascular mortality.

Suggested Citation

  • Kornelis J J van Hateren & Gijs W D Landman & Jarinke F H Arnold & Hanneke Joosten & Klaas H Groenier & Gerjan J Navis & Andrea Sparwasser & Stephan J L Bakker & Henk J G Bilo & Nanne Kleefstra, 2015. "Serum Proenkephalin A Levels and Mortality After Long-Term Follow-Up in Patients with Type 2 Diabetes Mellitus (ZODIAC-32)," PLOS ONE, Public Library of Science, vol. 10(7), pages 1-10, July.
    • Handle: RePEc:plo:pone00:0133065
    DOI: 10.1371/journal.pone.0133065
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0133065
    Download Restriction: no
    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0133065&type=printable
    Download Restriction: no
    File URL: https://libkey.io/10.1371/journal.pone.0133065?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. Michael Schemper & Robin Henderson, 2000. "Predictive Accuracy and Explained Variation in Cox Regression," Biometrics, The International Biometric Society, vol. 56(1), pages 249-255, March.
    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. Dimitris Rizopoulos, 2011. "Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data," Biometrics, The International Biometric Society, vol. 67(3), pages 819-829, September.
    2. Patrick Heagerty & Yingye Zheng, 2004. "Survival Model Predictive Accuracy and ROC Curves," UW Biostatistics Working Paper Series 1051, Berkeley Electronic Press.
    3. Matthias Schmid & Thomas Hielscher & Thomas Augustin & Olaf Gefeller, 2011. "A Robust Alternative to the Schemper–Henderson Estimator of Prediction Error," Biometrics, The International Biometric Society, vol. 67(2), pages 524-535, June.
    4. Eleni†Rosalina Andrinopoulou & Paul H. C. Eilers & Johanna J. M. Takkenberg & Dimitris Rizopoulos, 2018. "Improved dynamic predictions from joint models of longitudinal and survival data with time†varying effects using P†splines," Biometrics, The International Biometric Society, vol. 74(2), pages 685-693, June.
    5. P. Saha & P. J. Heagerty, 2010. "Time-Dependent Predictive Accuracy in the Presence of Competing Risks," Biometrics, The International Biometric Society, vol. 66(4), pages 999-1011, December.
    6. Janez Stare & Maja Pohar Perme & Robin Henderson, 2011. "A Measure of Explained Variation for Event History Data," Biometrics, The International Biometric Society, vol. 67(3), pages 750-759, September.
    7. Yingye Zheng & Patrick J. Heagerty, 2005. "Partly Conditional Survival Models for Longitudinal Data," Biometrics, The International Biometric Society, vol. 61(2), pages 379-391, June.
    8. Patrick J. Heagerty & Yingye Zheng, 2005. "Survival Model Predictive Accuracy and ROC Curves," Biometrics, The International Biometric Society, vol. 61(1), pages 92-105, March.
    9. Jialiang Li & Tonghui Yu & Jing Lv & Mei‐Ling Ting Lee, 2021. "Semiparametric model averaging prediction for lifetime data via hazards regression," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 70(5), pages 1187-1209, November.
    10. Daniel Commenges & Benoit Liquet & Cécile Proust-Lima, 2012. "Choice of Prognostic Estimators in Joint Models by Estimating Differences of Expected Conditional Kullback–Leibler Risks," Biometrics, The International Biometric Society, vol. 68(2), pages 380-387, June.
    11. Michael R. Crager, 2020. "Extensions of the absolute standardized hazard ratio and connections with measures of explained variation and variable importance," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 872-892, October.
    12. R. Schoop & E. Graf & M. Schumacher, 2008. "Quantifying the Predictive Performance of Prognostic Models for Censored Survival Data with Time-Dependent Covariates," Biometrics, The International Biometric Society, vol. 64(2), pages 603-610, June.
    13. Wolf, Petra & Schmidt, Georg & Ulm, Kurt, 2011. "The use of ROC for defining the validity of the prognostic index in censored data," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 783-791, July.
    14. Möst Lisa & Hothorn Torsten, 2015. "Conditional Transformation Models for Survivor Function Estimation," The International Journal of Biostatistics, De Gruyter, vol. 11(1), pages 23-50, May.

    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:pone00:0133065. 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.