Changes in diabetes prevalence and corresponding risk factors - findings from 3- and 6-year follow-up of PURE Poland cohort study
Bibliographic record
Abstract
BACKGROUND: Diabetes mellitus (DM) is one of the greatest challenges for public health worldwide. The aim of the study was the analysis of diabetes development in participants with normoglycemia and Impaired Fasting Glucose (IFG) in 3-year and 6-year follow-up of PURE Poland cohort study. METHODS: The analysis was conducted in Polish cohort enrolled into Prospective Urban and Rural Epidemiology (PURE) Study. The following study presents results of 1330 participants that have partaken both in the baseline study, in 3-year and in the 6-year follow up. The analysis of the impact of risk factors on diabetes development was performed using multivariate Cox frailty analysis. Population Attributable Risk (PAR) was computed individually for every risk factor. RESULTS: Diabetes prevalence increased from 17.7% at baseline to 23.98% in 3-year- and 28.27% in 6-year follow-up. The risk of diabetes was higher in participants with obesity [HR = 5.7, 95%Cl 2,56-12,82], overweight [HR = 3.4, 95%Cl 1,56-7,54] and IFG [HR = 2.7, 95%Cl 1,87-3,85]. The risk of diabetes development was almost 2-fold higher in men than in women [HR = 1.826; 95%CI =1,24 - 2,69]. In 6 years, diabetes developed in 23.8% of participants with IFG and 7.9% of participants with normoglycemia. According to PAR, overweight and obesity accounted for 80.8%, hypertension for 67.6% and IFG for 38.3% of diabetes cases in our population. CONCLUSIONS: Our study reveals alarming increase in prevalence of diabetes during 6 years of observation. In our population, most diabetes cases can be attributed to overweight, obesity, hypertension and IFG. Findings add strong rationale to implement targeted preventive measures in population of high risk.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".