Association of nutritional status with osteoporosis, sarcopenia, and cognitive impairment in patients on hemodialysis.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Inadequate nutrition in patients on hemodialysis causes various complications. This study aimed to investigate the association between nutritional status and risk of osteoporosis, sarcopenia, and cognitive impairment in patients on hemodialysis. METHODS AND STUDY DESIGN: We enrolled 131 older patients on maintenance hemodialysis. Geriatric Nutrition Risk Index (GNRI) was used to assess nutritional status. Patients were divided into quartile groups according to the GNRI. Dual-energy X-ray absorptiometry, bioimpedance analysis and handgrip strength measurement, and the Korean version of the Montreal Cognitive Assessment were used to assess osteoporosis, sarcopenia, and cognitive impairment, respectively. Biochemical laboratory tests were also performed before mid-week hemodialysis session. RESULTS: Patients from higher GNRI quartiles had a lower prevalence of osteoporosis and sarcopenia. Cognitive impairment was not associated with any GNRI quartile. In the multivariable models, longer dialysis periods (OR 1.696, 95% CI 1.053-2.729, p=0.030) and higher intact parathyroid hormone levels (OR 3.136, 95% CI 1.781-5.518, p<0.001) were significantly associated with osteoporosis risk. GNRI quartile 2 (OR 0.064, 95% CI 0.005-0.883, compared to quartile 1, p=0.040) and higher hemoglobin A1c levels (OR 3.728, 95% CI 1.033-86.4, p=0.043) were associated with a higher sarcopenia risk. Lower hemoglobin levels (OR 0.585, 95% CI 0.360-0.950, p=0.030) were associated with a higher risk of cognitive impairment. CONCLUSIONS: In patients on hemodialysis, inadequate nutrition was associated with the risk of osteoporosis and sarcopenia, but not cognitive impairment. Proper nutritional assessment and management in these patients could prevent complications related to bone and muscle loss.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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".