Achievement of treatment targets predicts progression of vascular complications in type 1 diabetes

Ilze Salna, Edgars Salna, Leonora Pahirko, Sabīne Skrebinska, Regīna Krikova, Inese Folkmane, Valdis Pīrāgs, Jelizaveta Sokolovska (Coresponding Author)

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Background and aim: To study the association between achievement of guideline-defined treatment targets on HbA1c, low-density lipoproteins (LDL-C), and blood pressure with the progression of diabetic complications in patients with type 1 diabetes (T1D). Methods: The study included 355 patients at baseline and 114 patients with follow-up data after 3–5 years. Outcome variables were the progression of diabetic kidney disease, retinopathy, or cardiovascular disease (CVD). We used logistic regression and other machine learning algorithms (MLA) to model the association of achievement of treatment targets and probability of progression of complications. Results: Achievement of the target blood pressure was associated with 96% lower odds of a new CVD event (0.04 (95% CI 0.00, 0.53), p = 0.016), and 72% lower odds of progression of any complication (0.28 (95% CI 0.09, 0.89), p = 0.027. Achievement of HbA1c target was associated with lower odds of composite complication progression by 82% (0.18 (95% CI 0.04, 0.88), p = 0.034.) None of the patients who achieved HbA1c target progressed in CVD. MLA demonstrated good accuracy for the prediction of progression of CVD (AUC 0.824), and lower accuracy for other complications. Conclusion: The achievement of blood pressure and HbA1c treatment targets is associated with lower odds of vascular complication of T1D in a real life study.

Original languageEnglish
Article number108072
JournalJournal of Diabetes and its Complications
Issue number12
Publication statusPublished - Dec 2021
Externally publishedYes


  • Complication
  • Machine learning
  • Treatment targets
  • Type 1 diabetes

Field of Science*

  • 3.2 Clinical medicine

Publication Type*

  • 1.1. Scientific article indexed in Web of Science and/or Scopus database


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