Thyroid Hormone Levels Correlate With the Maturation of Implanted Pancreatic Endoderm Cells in Patients With Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: Macroencapsulated pancreatic endoderm cells (PECs) can reverse diabetes in rodents and preclinical studies revealed that thyroid hormones in vitro and in vivo bias PECs to differentiate into insulin-producing cells. In an ongoing clinical trial, PECs implanted in macroencapsulation devices into patients with type 1 diabetes were safe but yielded heterogeneous outcomes. Though most patients developed meal responsive C-peptide, levels were heterogeneous and explanted grafts had variable numbers of surviving cells with variable distribution of endocrine cells. METHODS: We measured circulating triiodothyronine and thyroxine levels in all patients treated at 1 of the 7 sites of the ongoing clinical trial and determined if thyroid hormone levels were associated with the C-peptide or glucagon levels and cell fate of implanted PECs. RESULTS: Both triiodothyronine and thyroxine levels were significantly associated with the proportion of cells that adopted an insulin-producing fate with a mature phenotype. Thyroid hormone levels were inversely correlated to circulating glucagon levels after implantation, suggesting that thyroid hormones lead PECs to favor an insulin-producing fate over a glucagon-producing fate. In mice, hyperthyroidism led to more rapid maturation of PECs into insulin-producing cells similar in phenotype to PECs in euthyroid mice. CONCLUSION: These data highlight the relevance of thyroid hormones in the context of PEC therapy in patients with type 1 diabetes and suggest that a thyroid hormone adjuvant therapy may optimize cell outcomes in some PEC recipients.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".