Approaches to treatment 1: How is type 2 diabetes actually treated?
Bibliographic record
Abstract
Given the multiple organ systems playing roles in glucose production and utilization, with the gut, islets, liver, kidney, fat, muscle, and brain of particular importance, it is not surprising that the treatment of type 2 diabetes (T2D) is complex, but it is fascinating to review actual practice patterns. In the US, from 1999 to 2010, the use of glucose-lowering medication increased from 74% to 82% of individuals with diabetes, with metformin increasing from 35% to 55%, sulfonylureas decreasing from just over to just under 40%, thiazolidinediones increasing from 12% to 28%, the proportion receiving insulin increasing from 17% to 21%, and, in 2010, 8% receiving a dipeptidyl peptidase (DPP) 4 inhibitor (Fig. 1).1 Looked a differently, among some 20 million people diagnosed as having diabetes in the US, the number of prescriptions for non-insulin diabetes medicines issued annually increased from approximately 90 to 120 million from 2003 to 2012, with metformin increasing from 30 to 50 million, sulfonylureas around 30 million, thiazolidinediones decreasing from approximately 15 to 5 million, and both DPP-4 inhibitors and glucagon-like peptide-1 (GLP-1) analogs reaching approximately 8 million.2 In that study, only 45% of metformin was used as monotherapy; 22% was administered with sulfonylureas, 22% with a DPP-4 inhibitor, 10% with long-acting insulin analogs, 8% with a thiazolidinedione, and 4% with a GLP-1 analog; approximately two-thirds of use of sulfonylureas, DPP-4 inhibitors, and thiazolidinediones, and half of use of GLP-1 analogs, was with metformin.2 In Germany, in 2010, just 63.1% of people diagnosed as having diabetes received antihyperglycemic medication, of whom 40% received metformin, 20% sulfonylureas, 6% long-acting human insulin, 7% long acting analog insulin, 8% short-acting human insulin, 4% short-acting analog insulin, 6% human mixed insulin, 1% analog mixed insulin, 3% a DPP-4 inhibitor, and 1% a GLP-1 analog.3 In Canada, during the period from 1994 t o2006, less than half of individuals age 66 years and over diagnosed as having diabetes were treated during the first year after diagnosis, with the likelihood of treatment actually decreasing somewhat over the decade.4 The use of metformin as initial treatment increased from 20% to 80%, whereas the use of sulfonylureas decreased from 70% to 10%; thiazolidinediones, insulin, acarbose, and combinations were infrequently used.4 In the large US Kaiser Permanente database, from 2005 to 2010 there was an increase in treatment initiation during the first year after diabetes diagnosis from 36% to 44% (Fig. 2a), with metformin increasing from 36% to 44%, sulfonylureas decreasing from 31% to 10%, and the combination increasing from 5% to 10%; approximately 6% received insulin (Fig. 2b).5 What are we to make of these statistics? First, physicians appear to have concerns about initiating treatment at the time of diabetes diagnosis, in particular with newer diagnostic guidelines, either positively, because of understanding of the importance of emphasizing lifestyle modification, or negatively, because of skepticism about the value of such treatment at relatively modest levels of hyperglycemia, despite evidence from epidemiologic studies and randomized controlled trials that such treatment is appropriate.6 Second, there have been major changes in therapeutic approach over the past decade, with a marked reduction in the use of sulfonylureas, particularly as initial therapy, with an equally marked increase in the use of metformin, with increasing and then decreasing use of thiazolidinediones, and with the introduction of DPP-4 inhibitors and, to a lesser extent, GLP-1 analogs. The use of insulin is increasing somewhat, and comprises a major treatment approach, and combination treatment appears increasingly to be the rule rather than the exception in the management of hyperglycemia. With these perspectives, it will be interesting to consider current T2D treatment guidelines. This topic will be addressed in the next issue's Editorial.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".