ArticlePloS one2020
A statistical model to identify determinants of glycemic control in patients with type 2 diabetes with different pharmacotherapeutic profiles.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed, 7 citations in OpenAlex.
- Trial
- Integrated statistical and machine learning analysis provides insight into key influencing symptoms for distinguishing early-onset type 2 diabetes.Chronic diseases and translational medicine · 2022Article
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo develop a statistical model to identify determinants of glycemic control. MATERIALS AND
methodsA database was extracted from patients' records with at least one glycated hemoglobin (HbA1c) analysis and with antidiabetic therapy established and stabilized. A logistic regression model was designed to identify the statistical significance of factors associated with glycemic control.
resultsHigher probability of success (HbA1c ≤8% [64 mmol/mol]) was found for those who were older in age, those who were men, and those with higher education levels. Increased values for the following variables were associated with the poorest glycemic control: number of years of T2DM since diagnosis, number of antidiabetic medicines, body mass index, low-density lipoprotein cholesterol, triglycerides, systolic blood pressure and number of diabetes consultations in the last twelve months. The following pharmacotherapeutic treatments were associated with glycemic control (in decreasing order of the results): oral antidiabetic drugs; oral antidiabetic drugs and insulin; insulin. Patients using metformin and a dipeptidyl peptidase-4 inhibitors have a higher probability of success than do patients using metformin and a sulfonylurea, and patients using insulin and metformin have a higher probability of success than do patients using insulin alone.
conclusionsSociodemographic, clinical and therapeutic parameters can strongly affect glycemic control. Studies based on real-life patient data provide important information on the development of more effective glycemic control.
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