Obesity index and the risk of diabetes among Chinese women with prior gestational diabetes
Bibliographic record
Abstract
AIMS: There is some confusion regarding which anthropometric measurement of adiposity should be used to indicate diabetes, especially for Asians. The present study was to evaluate different indicators of adiposity (BMI, waist circumference and body fat) with Type 2 diabetes risk among women with prior gestational diabetes mellitus. METHODS: We performed a cross-sectional survey in 1263 women with gestational diabetes at 1-5 years after delivery. Logistic regression models were used to estimate the association of BMI, waist circumference and body fat with Type 2 diabetes risk. RESULTS: BMI, waist circumference and body fat were all associated with an increased risk of Type 2 diabetes among women with prior gestational diabetes (all P(trend) < 0.001). After adjustment for waist circumference and body fat, the positive association of BMI with Type 2 diabetes risk became non-significant and reversed. There was a significantly positive association of waist circumference with Type 2 diabetes risk after adjustment for BMI, and a significantly positive association of body fat with Type 2 diabetes risk after adjustment for both BMI and waist circumference. When the joint effects were examined, the significantly positive associations of waist circumference or body fat with Type 2 diabetes risk were consistent among women with different levels of BMI, and the positive association of BMI and Type 2 diabetes risk was significant among women with gestational diabetes with a waist circumference of ≥ 50% or body fat ≥ 50%. CONCLUSIONS: BMI, waist circumference and body fat were all associated with an increased risk of Type 2 diabetes, and waist circumference and body fat were better indicators than BMI for Type 2 diabetes risk among Chinese women with prior gestational diabetes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".