Effect of metabolic genetic variants on long-term disease comorbidity in patients with type 2 diabetes
Bibliographic record
Abstract
Underlying genetic determinants contribute to developing type 2 diabetes (T2D) future diseases. The present study aimed to identify which genetic variants are associated with the incident of the major T2D co-morbid disease. First, we conducted a discovery study by investigating the genetic associations of comorbid diseases within the framework of the Utrecht Cardiovascular Pharmacogenetic studies by turning information of > 25 years follow-up data of 1237 subjects whom were genotyped and included in the discovery study. We performed Cox proportional-hazards regression to examine associations between genetic variants and comorbid diseases including cardiovascular diseases (CVD), chronic eye disease, cancer, neurologic diseases and chronic kidney disease. Secondly, we replicated our findings in two independent cohorts consisting of 1041 subjects. Finally, we performed a meta-analysis by combining the discovery and two replication cohorts. We ascertained 390 (39.7%) incident cases of CVD, 182 (16.2%) of chronic eye disease, 155 (13.8%) of cancer, 31 (2.7%) of neurologic disease and 13 (1.1%) of chronic kidney disease during a median follow-up of 10.2 years. In the discovery study, we identified a total of 39 Single Nucleotide Polymorphisms (SNPs) associated with comorbid diseases. The replication study, confirmed that rs1870849 and rs8051326 may play a role in the incidence of chronic eye disease in T2D patients. Half of patients developed at least one comorbid disease, with CVD occurring most often and earliest followed by chronic eye disease. Further research is needed to confirm the associations of two associated SNPs with chronic eye disease in T2D.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".