#5200 IMPACT OF MINERAL BONE PARAMETERS IN COGNITIVE IMPAIRMENT IN DIALYSIS PATIENTS
Bibliographic record
Abstract
Abstract Background and Aims Mineral bone disease and cognitive impairment are related diseases in the CKD population. Vascular calcification, a marker of MBD, may play a role in the early detection of cognitive decline. This study aims to identify a relationship between MBD biomarkers and cognitive function and to identify dialysis patients with a high risk of dementia. Method A total of 98 patients participated in this cross-sectional study, with 63 on hemodialysis and 35 on peritoneal dialysis. They underwent the Montreal Cognitive Assessment (MoCA) questionnaire, which categorized mild, moderate, severe, and severe based on scoring. PTH, P, Ca, ALP, and Mg serum concentrations and vascular calcification were measured as MBD biomarkers. The Adragaos score was applied to graphs of the hands and pelvis to evaluate vascular calcification. Results The mean MoCA score was 20,28±5.8. Based on the responses, it was concluded that 70% of HD patients had mild cognitive impairment, compared to 63% of PD patients. According to the descriptive data, patients whose underlying CKD was caused by nephroangisclerosis had the lowest MOCA test results compared to other groups (p<0.001). The degree of calcification was observed to have an adverse effect on cognitive performance in both groups (p<0.012); the high Adragaos score contributed to the decline in the MoCA test score. In multivariate logistic regression analysis, hypercalcemia and hypomagnesemia were independent variables for cognitive function impairment (p = 0.006 and p = 0.04, respectively). The serum concentration of PTH (p = 0.008) and ALP (p = 0.001) were found to be risk factors for HD patients during the evaluation of the MoCA test in each of the groups. Conclusion Vascular calcifications are considered a risk factor for cognitive impairment. In our study, vascular calcification risk is positively impacted by indicators such as hyperparathyroidism, hypercalcemia, high levels of ALP, and hypomagnesemia. Recently research has demonstrated the effectiveness of magnesium as a vascular calcification inhibitor. So, nephrologists should be more careful in monitoring levels of MBD biomarkers.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.004 | 0.001 |
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".