Artificial Intelligence (AI) approach to identifying factors that determine systolic blood pressure in type 2 diabetes (study from the LOOK AHEAD cohort).

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  • Author(s): Khthir R;Khthir R; Santhanam P; Santhanam P
  • Source:
    Diabetes & metabolic syndrome [Diabetes Metab Syndr] 2021 Nov-Dec; Vol. 15 (6), pp. 102278. Date of Electronic Publication: 2021 Sep 11.
  • Publication Type:
    Journal Article
  • Language:
    English
  • Additional Information
    • Source:
      Publisher: Elsevier Ltd Country of Publication: Netherlands NLM ID: 101462250 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1878-0334 (Electronic) Linking ISSN: 18714021 NLM ISO Abbreviation: Diabetes Metab Syndr Subsets: MEDLINE
    • Publication Information:
      Original Publication: Amsterdam : Elsevier Ltd.
    • Subject Terms:
      Artificial Intelligence* ; Blood Pressure*; Albumins/*analysis ; Biomarkers/*blood ; Creatinine/*blood ; Diabetes Mellitus, Type 2/*physiopathology ; Hypertension/*diagnosis; Adult ; Aged ; Blood Pressure Determination ; Exercise ; Female ; Follow-Up Studies ; Humans ; Hypertension/epidemiology ; Male ; Middle Aged ; Prognosis ; Randomized Controlled Trials as Topic ; United States/epidemiology
    • Abstract:
      Background and Aims: Artificial Intelligence (AI) methods have recently become critical for research in diabetes in the era of big-data science.
      Methods: In this study, we used the data from the LOOK AHEAD and applied Random Forest to examine the factors determining SBP in persons with diabetes using the software R (version 4.0.3).
      Results: Our analysis (that included 4723 participants) showed that maximal exercise capacity, age, albumin to creatinine ratio, and serum creatinine were the key variables that determined systolic blood pressure.
      Conclusions: Maximum exercise capacity is an important predictor of systolic blood pressure in patients with type 2 diabetes.
      Competing Interests: Declaration of competing interest The author of the manuscript has no disclosures to make and report no conflict of interest.
      (Copyright © 2021 Diabetes India. Published by Elsevier Ltd. All rights reserved.)
    • Contributed Indexing:
      Keywords: Artificial intelligence; Diabetes mellitus; Predictors; Systolic blood pressure
    • Accession Number:
      0 (Albumins)
      0 (Biomarkers)
      AYI8EX34EU (Creatinine)
    • Publication Date:
      Date Created: 20210925 Date Completed: 20220307 Latest Revision: 20220307
    • Publication Date:
      20221213
    • Accession Number:
      10.1016/j.dsx.2021.102278
    • Accession Number:
      34562867