Bone Mineral Metabolism and Muscle Alterations in Non-dialysis Dependent Patients With Chronic Kidney Disease
Bibliographic record
Abstract
Abnormal bone mineral density (BMD) is common in chronic kidney disease (CKD) and related with higher risk of disease progression, cardiovascular disease, and mortality. The aim of this study was to assess BMD, its change overtime and association with body composition and biochemical parameters of mineraly metabolism. This was a longitudinal study of patients with NDD-CKD (stages 3–5) undergoing interdisciplinary treatment at an outpatient Nephrology Clinic and instructed to follow a low protein diet. Dual energy X-ray absorptiometry (DXA) was performed to estimate body composition and BMD (T-score). Mineral metabolism parameters included parathormone (PTH), calcium, phosphorus and vitamin D. Glomerular filtration rate was estimated (eGFR) by the CKD-EPI equation. Osteopenia was defined as T-score < -1.0. Baseline and follow-up comparisons between groups with and without osteopenia were performed by two-way ANOVA. Correlations were adjusted by sex, age and eGFR. Forty-five patients (56% males) aged 64.4 ± 9.9 y and eGFR 31.4 ± 10.9 ml/min completed a follow-up of ∼3 years (2.7 ± 1.3). As expected, a reduction in renal function was observed (median = −1.10 ml/min; 95% CI: −8.8 to 0.64, P < 0.05). BMD and appendicular skeletal muscle (ASM) decreased: 1.06 ± 0.15 vs. 1.05 ± 0.03g/cm2 (P = 0.03) and 20.3 ± 4.6 vs. 18.9 ± 0.8kg (P = 0.01), respectively. Prevalence of osteopenia was 42.2% with no significant change overtime. Patients with osteopenia presented with higher (P < 0.0001) change in ASM (median: −1.58kg; 95% CI: −3.8 to 0.66 vs. −0.83; −4.0 to 2.4) and in LST (−1.08 kg; −5.0 to 2.8 vs. 0.88; −4.6 to 6.3), compared with patients without osteopenia. Changes in eGFR and mineral metabolism parameters were similar between groups. T-score change was negatively correlated with change in LST (r = 0.66; P = 0.04) and PTH (r = −0.70; P = 0,03), and with baseline LST (r = −0.35; P = 0.04) independent of age, sex and eGFR. Body fat increased (22.7 kg ± 8.1 vs. 23.8 kg ± 8.7; P = 0.04) during follow up, but it was not significantly correlated with T-score. Prevalence of osteopenia was high in patients with NDD-CKD, and BMD decreased after 3 years, which was associated with a reduction in LST and increase in PTH, independent of eGFR, age, and sex. LST should be monitored in NDD-CKD to prevent risk for abnormal BMD. FAPERJ
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".