One-hour postload glucose levels predict mortality from cardiovascular diseases and malignant neoplasms in healthy subjects
Bibliographic record
Abstract
Abstract Little is known about biological markers, at levels within their normal ranges which might predict future mortality. We aimed at identifying possible predictors of future death in participants before pathological conditions manifest. We analyzed data from a population-based prospective cohort study (the Ohasama study), comprised of 993 participants who underwent 75-g oral glucose tolerance tests (OGTTs). We collected blood parameters, including those measured during OGTTs, and divided the study population into two groups based on the median value of each parameter, followed by analyses of mortality during follow-up in both groups. In addition, we extracted subjects with normal glucose tolerance (NGT) (n = 595) and analyzed the association between 1-h postload plasma glucose during OGTTs (1-hrPG) and mortality as well as the causes of death. Among all parameters evaluated, 1-hrPG was found to be most significantly associated with all-cause mortality during the mean follow-up of 14.3 years. When we focused on subjects with NGT, Harrell's C concordance index analysis revealed a cut-off of 1-hrPG ≥170 mg/dL to be most strongly associated with all-cause mortality (0.8066). The Kaplan–Meier plots showed nearly double the proportion of the 1-hrPG ≥170 group to have died as compared with the 1-hrPG <170 group throughout the follow-up period after the third year. Cardiovascular diseases and malignant neoplasms both strongly contributed to the increased mortality in the high 1-hrPG group. Thus, 1-hrPG ≥170 is a powerful predictor of future death in subjects with NGT. Atherosclerotic and malignant diseases both contributed to the increased mortality.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".