Meta‐analysis of factors associated with antidiabetic drug prescribing for type 2 diabetes mellitus
Bibliographic record
Abstract
BACKGROUND: There is a lack of consensus on prescribing alternatives to initial metformin therapy and intensification therapy for type 2 diabetes mellitus (T2DM) management. This review aimed to identify/quantify factors associated with prescribing of specific antidiabetic drug classes for T2DM. METHODS: Five databases (Medline/PubMed, Embase, Scopus, Web of Science) were searched using the synonyms of each concept (patients with T2DM, antidiabetic drugs and factors influencing prescribing) in both free text and Medical Subject Heading (MeSH) forms. Quantitative observational studies evaluating factors associated with antidiabetic prescribing of metformin, sulfonylurea, thiazolidinedione, Dipeptidyl-peptidase 4 inhibitors (DPP4-I), sodium glucose transporter 2 inhibitors (SGLT2-I), Glucagon-Like peptide receptor agonist (GLP1-RA) and insulin in outpatient settings and published from January 2009 to January 2021 were included. Quality assessment was performed using a Newcastle-Ottawa scale. The validation was done for 20% of identified studies. The pooled estimate was measured using a three-level random-effect meta-analysis model based on odds ratio [95% confidence interval]. Age, sex, body mass index (BMI), glycaemic control (HbA1c) and kidney-related problems were quantified. RESULTS: Of 2331 identified studies, 40 met the selection criteria. Of which, 36 and 31 studies included sex and age, respectively, while 20 studies examined baseline BMI, HbA1c and kidney-related problems. The majority of studies (77.5%, 31/40) were rated as good and despite that the overall heterogeneity for each studied factor was more than 75%, it is mostly related to within-study variance. Older age was significantly associated with higher sulfonylurea prescription (1.51 [1.29-1.76]), yet lower prescribing of metformin (0.70 [0.60-0.82]), SGLT2-I (0.57 [0.42-0.79]) and GLP1-RA (0.52 [0.40-0.69]); while higher baseline BMI showed opposite significant results (sulfonylurea: 0.76 [0.62-0.93], metformin: 1.22 [1.08-1.37], SGLT2-I: 1.88 [1.33-2.68], and GLP1-RA: 2.35 [1.54-3.59]). Both higher baseline HbA1c and having kidney-related problems were significantly associated with lower metformin prescription (0.74 [0.57-0.97], 0.39 [0.25-0.61]), but more insulin prescriptions (2.41 [1.87-3.10], 1.52 [1.10-2.10]). Also, DPP4-I prescriptions were higher for patients with kidney-related problems (1.37 [1.06-1.79]) yet lower among patients with higher HbA1c (0.82 [0.68-0.99]). Sex was significantly associated with GLP1-RA and thiazolidinedione prescribing (F:M; 1.38 [1.19-1.60] and 0.91 [0.84-0.98]). CONCLUSION: Several factors were identified as potential determinants of antidiabetic drug prescribing. The magnitude and significance of each factor differed by antidiabetic class. Patient's age and baseline BMI had the most significant association with the choice of four out of the seven studied antidiabetic drugs followed by the baseline HbA1c and kidney-related problems which had an impact on three studied antidiabetic drugs, whereas sex had the least impact on prescribing decision as it was associated with GLP1-RA and thiazolidinedione only.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".