Tetraspanin 7 autoantibodies predict progressive decline of beta cell function in individuals with LADA
Bibliographic record
Abstract
AIMS/HYPOTHESIS: The aim of this work was to investigate whether tetraspanin 7 autoantibodies (TSPAN7A) are valuable in predicting poor beta cell function in individuals with latent autoimmune diabetes in adults (LADA). METHODS: The cross-sectional study involved participants with LADA (n = 173), type 1 diabetes (n = 158), type 2 diabetes (n = 204) and healthy control participants (n = 170). The longitudinal study involved 53 participants with LADA, with a 3-year follow-up. In both cohorts, TSPAN7A in the sera were measured by a luciferase immunoprecipitation system assay, and physical and clinical characteristics were recorded. RESULTS: The prevalence of TSPAN7A in LADA, type 1 diabetes, type 2 diabetes and healthy control participants was 21.4% (37/173), 26% (41/158), 0.5% (1/204) and 1.2% (2/170), respectively. Importantly, measurement of TSPAN7A significantly increased the number of individuals with LADA found to be positive for multiple antibodies (32.4% vs 22%; p < 0.001). Further logistic regression analysis demonstrated that positivity for TSPAN7A (OR 2.87, p = 0.034), disease duration (OR 1.83, p = 0.019) and GAD antibody titre (OR 2.67, p = 0.009) were risk factors for beta cell function in LADA, while BMI (OR 0.34, p = 0.001) was a protective factor. In the prospective study in individuals with LADA, the median annual decrease in rates of fasting C-peptide and 2 h postprandial C-peptide in individuals who were positive for TSPAN7A was significantly higher when compared with the decrease in those who were negative for TSPAN7A (34.6% vs 7.9%, p = 0.043 and 33.2% vs 11%, p = 0.041, respectively). CONCLUSIONS/INTERPRETATION: TSPAN7A are valid islet autoantibodies for use in East Asian populations with autoimmune diabetes and can discriminate individuals with LADA who have lower beta cell function after disease progression.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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".