Hemoglobin A1c and abdominal obesity as predictors of diabetes and ASCVD in individuals with prediabetes in UK Biobank: a prospective observational study
Bibliographic record
Abstract
OBJECTIVES: Whether "prediabetes" merits particular clinical attention beyond the management of associated risk factors is controversial, particularly given the expansion of the definition of prediabetes from HbA1c 6.0-6.4% to 5.7-6.4%. Accordingly, we compared the risk of atherosclerotic cardiovascular disease (ASCVD) and type II diabetes mellitus (DM) risk in male and female participants with prediabetes and HbA1c 5.7-6.0% (low) versus 6.1-6.4% (high) to examine whether preventive recommendations should prioritize treating blood sugar or obesity, the major determinants of risk of DM versus other causes of ASCVD, such as lipids and blood pressure. RESEARCH DESIGN AND METHODS: 10-year risks of ASCVD and DM risk were determined separately in 296,470 women and men, age 40-73, from UK Biobank, free of ASCVD and DM at baseline. Cox proportional hazards regression with adjustment for conventional risk factors and Kaplan-Meier estimators were used with low (HbA1c 5.7-6.0%) and high prediabetes (HbA1c 6.1-6.4%) as primary exposuress with further stratification and adjustment for waist circumference. RESULTS: In multivariate-adjusted models, low and high prediabetes was associated with increased risk of ASCVD versus normal HbA1c in both women (HR = 1.08, 95% CI 1.01,1.15 in low prediabetes and 1.25, 95% CI 1.14,1.38 in high prediabetes) and men (HR = 1.18, 95%CI 1.11,1.24 in low prediabetes and 1.27, 95% CI 1.17,1.38 in high prediabetes). The associations with new onset DM were substantially more potent, achieving HR of 4.05, 95%CI 3.73,4.40 in low prediabetic women versus 14.22, 95% CI 13.06,15.49 in high pre-diabetic women and 4.45, 95% CI 4.12,4.80 in low prediabetic men versus 15.59, 95% CI 14.43,16.85 in high pre-diabetic men. Furthermore, increasing waist circumference in low prediabetic men and all prediabetic women was associated with meaningful increase in DM risk. CONCLUSIONS: The risks of progression to both new onset DM and ASCVD are significantly greater in the prediabetic population. This underscores the importance of preventing the development of DM and efforts to reduce cardiometabolic risk through optimizing multiple risk factors in both categories of prediabetes. Risk modification by waist circumference suggests weight and glucose lowering therapies should be targeted at those with highest risks.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".