Lifestyle factors and reversion to normoglycaemia by prediabetes type in PREDAPS study.
Bibliographic record
Abstract
Abstract Background: Healthy lifestyle interventions and drug therapies are proven to have a positive preventative influence on normal glucose regulation in prediabetes; however, there is little evidence to support the role of these factors according to the various stage of the prediabetes state. Aims : This study aims to investigate the role of lifestyle factors on the reversion to normal glucose regulation according to the different stage of the prediabetes state based on most up-to-date American Diabetes Association (ADA) guidelines. Design and Setting: Observational prospective cohort study. The Cohort study in Primary Health Care on the Evolution of Patients with Prediabetes from 2012-2015 Methods: A total of 1184 individuals aged 30 to 74 years old were included and classified based on the ADA in three mutually exclusive groups using either fasting plasma glucose (FPG) levels (from 100-125 mg/dl, FPG group), (HbA 1c (5.7%–6.4%, HbA1c group) or both impaired parameters group. Information on lifestyle factors and biochemical parameters were collected at baseline Reversion to normal glucose regulation was calculated at third year of follow-up. Relationship of lifestyle factor and type of prediabetes with reversion were estimated using odds ratios (ORs) with 95% confidence intervals (CIs) adjusting by different groups of confounders. Results: Proportion of reversion rates were 31% for FPG group, 31% for HbA1c group and 7.9% for both altered parameters group, respectively. Optimal life style factors such as BMI<25 kg/m 2 [OR (95% CI): 1.90 (1.20-3.01)], high adherence to Mediterranean diet 1.78 (1.21-2.63) and absence of abdominal obesity 1.70 (1.19-2.43) were the strongest predictors for reversion to normal glucose. ORs of reversion to normal glucose were 4.87 (3.10-7.65) for FPG group and 3.72 (2.39-5.78) for HbA1c group, taking as reference those with both impaired parameters. These estimates remained almost the same after further adjustment for biochemical parameters and lifestyle factors. Conclusions: Although optimal lifestyle factors showed to be a positive predictor for reversion to normal glucose regulation, they do not seem to explain the differences according to the type of prediabetes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".