Effects of islet transplantation on microvascular and macrovascular complications in type 1 diabetes
Bibliographic record
Abstract
Type 1 diabetes is associated with high morbidity and mortality from microvascular and macrovascular disease with considerable economic cost to society. Islet cell transplantation (ICT) is a treatment option recommended by National Institute for Health and Care Excellence (NICE) for people with debilitating hypoglycaemia due to type 1 diabetes, including those with renal failure where kidney transplantation may also be indicated. The primary aim of ICT is to improve glycaemic control, reduce severe hypoglycaemia, stabilise glycaemic variability and restore awareness of hypoglycaemia where this is compromised. Insulin independence, although not a primary aim, should also be considered a therapeutic goal. The impact ICT has on the progression of microvascular and macrovascular diabetes complications is derived from small studies and has not been examined in large clinical trials. Lifelong immunosuppression, which is necessary to avoid transplant rejection, has adverse effects on lipid metabolism, hypertension and renal function, which must also be considered. In this review, we discuss the role of ICT in type 1 diabetes management and the available evidence with respect to microvascular and macrovascular disease progression post-transplantation. We conclude that, following ICT, microvascular complications including retinopathy and neuropathy are stabilised or improved. Effects on nephropathy can be complicated by coexisting kidney transplantation and the impact of immunosuppression, the latter leading to an early decline in renal function; however, there is evidence to suggest stable renal outcomes in the long term. Short-term studies have demonstrated a positive impact of ICT on surrogate markers of macrovascular disease; however, long-term studies and trials in this area are lacking.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".