Longitudinal analysis of serum urate in prediabetic phase
Bibliographic record
Abstract
OBJECTIVES: Despite the well-established association between prediabetes and hyperuricaemia, knowledge about serum urate (SU) trends during the prediabetic phase is limited. Therefore, we aimed to assess the longitudinal changes of SU in individuals with prediabetes. METHODS: Individuals with prediabetes, defined by initial haemoglobin A1c (HbA1c) levels between 5.7% and 6.4%, were identified using electronic health records from an academic health system (2007-2022). We required at least one SU test before and after the prediabetes diagnosis. The primary outcome was the longitudinal SU trends during the follow-up period, estimated with a multivariable mixed-effects model. Patients were censored at diabetes onset. Marginal effects of covariates on SU changes were estimated. Subsequent analyses examined SU variations in subgroups stratified by age, sex, BMI, HbA1c, estimated glomerular filtration rate and metformin use. RESULTS: Out of 25 526 individuals with prediabetes, 1521 met the SU cohort requirements, contributing to 6832 SU observations. At baseline, median age was 63 years and 40% were female. Median values were SU 6.3 mg/dl, HbA1c 5.9% and BMI 30 kg/m2. Median follow-up was 7.4 years. Older age, male sex, greater BMI and higher HbA1c were significant predictors of increased longitudinal SU levels. Individuals with a BMI ≥30 kg/m2 exhibited higher SU levels compared with those with lower BMI values. CONCLUSION: Among individuals with prediabetes, several baseline variables were significant predictors of increased SU levels over time. These longitudinal trends in SU, support the potential for early intervention during the prediabetic phase, possibly reducing the risk of gout.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".