Non-obese patients with type 2 diabetes and prediabetic subjects: distinct phenotypes requiring special diabetes treatment and (or) prevention?
Bibliographic record
Abstract
A major reason for the increased incidence of type 2 diabetes mellitus (T2DM) across the world is the so-called obesity epidemic, which occurs both in developed and developing countries. However, a large proportion of patients with T2DM in European and, in particular, Asian countries are non-obese. The non-obese T2DM phenotype is characterized by disproportionally reduced insulin secretion and less insulin resistance, as compared with obese patients with T2DM. Importantly, non-obese patients with T2DM have a similar increased risk of cardiovascular disease as obese T2DM patients. The risk of T2DM in non-obese patients is influenced by genetics as well as factors operating in utero indicated by low birth weight. Furthermore, this phenotype is slightly more prevalent among patients with latent autoimmune diabetes in adults, characterized by positive anti-GAD antibodies. The recently identified TCF7L2 gene polymorphism resulting in low insulin secretion influences the risk of T2DM in both obese and non-obese subjects, but is relatively more prevalent among non-obese patients with T2DM. Furthermore, the Pro12Ala polymorphism of the PPAR gamma gene influencing insulin action increases the risk of T2DM in non-obese subjects. Despite a "normal" body mass index, non-obese patients with T2DM are generally characterized by a higher degree of both abdominal and total fat masses (adiposity). Prevention of T2DM with lifestyle intervention is at least as effective in non-obese as in obese prediabetic subjects, and recent data suggest that metformin treatment targeting insulin resistance and non-glycemic cardiovascular disease risk factors is as beneficial in non-obese as in obese patients with T2DM. Nevertheless, non-obese patients with T2DM may progress to insulin treatment more rapidly as compared with obese patients with T2DM.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".