Prevalence and Correlates of Latent Autoimmune Diabetes in Adults in Tianjin, China
Bibliographic record
Abstract
OBJECTIVE: Data on latent autoimmune diabetes in adults (LADA) from population-based studies are sparse. We sought to investigate the prevalence and correlates of LADA. RESEARCH DESIGN AND METHODS: A total of 8,109 participants, who were aged ≥15 years and living in Tianjin, China, were assessed to identify individuals with type 2 diabetes (American Diabetes Association Criteria, 1997) and further to detect patients with LADA. LADA was ascertained by 1) the presence of type 2 diabetes and age ≥35 years, 2) the lack of a requirement for insulin at least 6 months after the diagnosis of type 2 diabetes, and 3) serum GAD antibody positivity. Data were analyzed using multinomial logistic regression with adjustment for potential confounders. RESULTS: Of all participants, 498 (6.1%) were patients with type 2 diabetes. Of them, 46 (9.2%) were found to have LADA. The prevalence of LADA was 0.6% (46 of 8,109), and tended to increase with age up to 50-59 years in all participants. The odds ratios (95% CI) of LADA related to hypertension, family history of diabetes, waist-to-hip ratio ≥0.85, and major stressful events were 1.93 (1.02-3.65), 17.59 (9.08-34.06), 5.37 (2.31-12.49), and 4.09 (1.75-9.52), respectively. CONCLUSIONS: The prevalence of LADA is ∼9% in patients with type 2 diabetes. Hypertension, family history of diabetes, central obesity, and major stressful events may be associated with the occurrence of LADA.
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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".