Advertisement

Comments on Chronic Kidney Disease, Basal Insulin Glargine, and Health Outcomes in People with Dysglycemia: The Origin Study

  • Saeid Safiri
    Affiliations
    Managerial Epidemiology Research Center, Department of Public Health, School of Nursing and Midwifery, Maragheh University of Medical Sciences, Maragheh, Iran

    Department of Epidemiology & Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran
    Search for articles by this author
      To the Editor:
      I was interested to read the paper by Papademetriou et al
      • Papademetriou V.
      • Nylen E.S.
      • Doumas M.
      • et al.
      Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN study.
      published in the December 2017 issue of The American Journal of Medicine. I would like to congratulate the authors on their published results in the high-quality journal The American Journal of Medicine. However, several methodological issues should be considered.
      The authors defined composite cardiovascular outcomes, in which specific outcomes including nonfatal myocardial infarction, nonfatal stroke, or death from cardiovascular causes are combined into a broader category.
      • Papademetriou V.
      • Nylen E.S.
      • Doumas M.
      • et al.
      Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN study.
      Composite outcomes are frequently used in biomedical research as primary outcome. Such outcomes will be defined to increase sample size because often, the data are sparsely distributed across specific outcomes.
      • Steyerberg E.W.
      Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating.
      With defining of composite outcomes, we expect that effect size for relationship between exposure and composite outcome is generalizable for relationship between exposure and all specific outcomes. In other words, whether a hazard ratio of 1.87 for effect of mild to moderate chronic kidney disease on the studied primary outcome
      • Papademetriou V.
      • Nylen E.S.
      • Doumas M.
      • et al.
      Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN study.
      is generalizable to the effect of mild to moderate chronic kidney disease on stroke or myocardial infraction. An alternative to combining specific outcomes into a broader category is to use hierarchical regression method to model relationship between an exposure and specific outcome separately and simultaneously.
      • Richardson D.B.
      • Hamra G.B.
      • MacLehose R.F.
      • Cole S.R.
      • Chu H.
      Hierarchical regression for analyses of multiple outcomes.
      Moreover, the difference between resulting estimates in model 1 (univariable model) and other models (multivariable models) was negligible.
      • Papademetriou V.
      • Nylen E.S.
      • Doumas M.
      • et al.
      Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN study.
      Here, it seems that there is a degree of residual confounding in resulting estimates in the study.

      References

        • Papademetriou V.
        • Nylen E.S.
        • Doumas M.
        • et al.
        Chronic kidney disease, basal insulin glargine, and health outcomes in people with dysglycemia: the ORIGIN study.
        Am J Med. 2017; 130: 1465.e27-1465.e39
        • Steyerberg E.W.
        Clinical Prediction Models: A Practical Approach to Development, Validation, and Updating.
        Springer Science & Business Media, New York, NY2008
        • Richardson D.B.
        • Hamra G.B.
        • MacLehose R.F.
        • Cole S.R.
        • Chu H.
        Hierarchical regression for analyses of multiple outcomes.
        Am J Epidemiol. 2015; 182: 459-467