Downregulation of T-Cell Transcription Factors in Adult Latent Autoimmune Diabetes with High-Titer Glutamic Acid Decaroxylase Antibody
Bibliographic record
Abstract
INTRODUCTION: Latent autoimmune diabetes in adults (LADA) shows a heterogeneous clinical profile that is dependent on the glutamic acid decaroxylase antibody (GADA) titer. We speculated that LADA patients with a high or low GADA titer may have distinct T-lymphocyte subset profiles and distinct expression patterns of transcription factors involved in T-cell immunomodulation. METHODS: Patients with LADA (n = 40) and type 2 diabetes (T2DM; n = 14) were recruited to the study, and peripheral blood mononuclear cells were isolated. The proportions of T-lymphocyte subsets (Th1 [T helper type 1], Th2 [T helper type 2], Treg [regulatory T], and Th17 [T helper type 17] cells) were determined by flow cytometry. Real-time polymerase chain reaction (PCR) was performed to estimate mRNA expression levels of the T-cell subtype-enriched transcription factors T-bet (Th1), GATA3 (Th2), transcription factor forkhead box protein 3 (FOXP3) (Treg), and RORC (Th17). RESULTS: T cells) was greater in the LADA patients with high-titer GADA than in the LADA patients with low-titer GADA (11.06 ± 1.62 vs. 7.05 ± 0.86, P = 0.030). Compared to the T2DM group, in the low-titer GADA group the frequency of Th1 was significantly reduced (7.05 ± 0.86 vs. 16.75 ± 3.73, P = 0.017) and the frequency of Th17 frequency was signficantly increased (1.11 ± 0.09 vs. 0.74 ± 0.16, P = 0.017). Compared to T2DM patients, in the high-titer GADA group there was a significantly reduced expression of FOXP3 (0.35 ± 0.13 vs. 1.75 ± 0.54, P = 0.002), RORC (0.53 ± 0.19 vs. 2.00 ± 0.77, P = 0.046), and GATA3 (0.74 ± 0.17 vs. 2.31 ± 0.91, P = 0.046). Similarly, the high-titer GADA group expressed reduced levels of FOXP3 and RORC compared to the low-titer GADA group (0.35 ± 0.13 vs. 1.50 ± 0.41, P = 0.027; 0.53 ± 0.19 vs. 1.35 ± 0.21, P = 0.027, respectively). There was a negative correlation between FOXP3 expression level and GADA titer for the entire cohort (r = - 0.0433, P = 0.015) and a stronger negative correlation in LADA patients (r = - 0.606, P = 0.008). CONCLUSION: LADA patients with high-titer GADA express lower levels of T-cell transcription factors, including the Treg transcription factor FOXP3, which may contribute to differences in the clinical profile compared to LADA patients with low-titer GADA. TRIAL REGISTRATION: ClinicalTrials.gov identifier, NCT01159847.
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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 | 0.000 | 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) | 0.001 | 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".