Minireview: Understanding and targeting inflammatory, hemodynamic and injury markers for cardiorenal protection in type 1 diabetes
Bibliographic record
Abstract
The coexistence of cardiovascular disease (CVD) and diabetic kidney disease (DKD) is common in people with type 1 diabetes (T1D) and is strongly associated with an increased risk of morbidity and mortality. Hence, it is imperative to explore robust tools that can accurately reflect the development and progression of cardiorenal complications. Several cardiovascular and kidney biomarkers have been identified to detect at-risk individuals with T1D. The primary aim of this review is to highlight biomarkers of injury, inflammation, or renal hemodynamic changes that may influence T1D susceptibility to CVD and DKD. We will also examine the impact of approved pharmacotherapies for type 2 diabetes, including renin-angiotensin-aldosterone system (RAAS) inhibitors, sodium-glucose cotransporter-2 (SGLT2) inhibitors and glucagon-like peptide-1 receptor agonists (GLP-1RAs) on candidate biomarkers for cardiorenal complications in people with T1D and discuss how these changes may potentially mediate kidney and cardiovascular protection. Identifying predictive and prognostic biomarkers for DKD and CVD may highlight potential drug targets to attenuate cardiorenal disease progression, implement novel risk stratification measures in clinical trials, and improve the assessment, diagnosis, and treatment of at-risk individuals with T1D.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".