Nutritional Phenotypes and Variation in Nutritional Parameter Trajectories Among Non-Dialysis CKD (CKD-ND) Patients Prescribed Oral Nutritional Supplements
Bibliographic record
Abstract
Background: Among CKD-ND patients at risk of undernutrition/protein-energy wasting, the definition of patient subgroups most likely to benefit from ONS treatment is not known. Therefore, our aims were to identify phenotypes of non-dialysis CKD patients prescribed ONS, and to assess nutritional parameter slopes before and after ONS use, by phenotype. Methods: This longitudinal cohort study included 2543 adult CKD-ND patients who entered multidisciplinary CKD clinics across British Columbia during 2010- 2019, met weight and/or dietary intake criteria for ONS prescription based on dietitian assessment, received ≥1 ONS prescription. Hierarchical cluster analysis was used to identify phenotypes using baseline nutritional parameters. Using linear mixed models, slopes for body mass index (BMI), serum albumin, bicarbonate, phosphate, and neutrophil-to-lymphocyte ratio (NLR), an inflammation marker, were assessed in the 2-year periods before and after the first ONS prescription. Results: Cluster analysis identified five nutritional phenotypes. Changes in parameter slopes (Δslope = post-ONS slope - pre-ONS slope) varied by cluster (Figure). Cluster 1 (characterized by the highest mean NLR and the lowest mean BMI among clusters) demonstrated statistically significant positive Δslopes for BMI, albumin and bicarbonate, and a negative Δslope for NLR. Cluster 2 (hypoalbuminemia) demonstrated positive Δslopes for BMI, albumin, and phosphate. Cluster 3 (low mean BMI) demonstrated a positive Δslope for BMI, accompanied by negative Δslopes for albumin and bicarbonate, and a positive Δslope for NLR. Cluster 4 (acidosis) demonstrated positive Δslopes for BMI and bicarbonate. In Cluster 5 (highest BMI), a negative Δslope for albumin and a positive Δslope for NLR were observed (no improvement with ONS). Conclusions: The variation in response to ONS by cluster subgroup lends support to an individualized approach to nutritional management of patients at risk of undernutrition/protein-energy wasting. Funding: Government Support - Non-U.S.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".