Prevalence and characteristics of people with a high body mass index across the kidney disease spectrum: a population-based cohort study
Bibliographic record
Abstract
The prevalence of obesity is growing globally and has major health implications, particularly for those with chronic kidney disease (CKD). People with obesity have a higher risk of CKD progression and face barriers to transplantation. Studies on the epidemiology and outcomes of obesity in the Canadian CKD population are lacking. This population-based cohort study included adults aged 18 and over with a hospital encounter in London, Ontario, Canada from 2010 to 2019 where height and weight were recorded. They were stratified into CKD stages 1-5 (by estimated glomerular filtration rate (eGFR)), dialysis or renal transplant status and by body mass index (BMI) using Canada Obesity Guidelines. Outcomes included prevalence of BMIs by CKD stage, and CKD progression and transplant outcomes across BMI classes. Our cohort included 198,151 patients. The prevalence of BMI ≥30 kg/m2 grew as eGFR declined (i.e., 37% in stage 1, 41.5% in stage 3b, 40.9% in stage 4) but fell in end-stages (i.e., 37.4% in Stage 5, 38.8% in dialysis, 38.5% in transplant recipients). CKD progression and kidney failure appeared more frequent in those with a BMI ≥30 kg/m2. In a smaller sample, we noted that end-stage patients with a BMI ≥30 kg/m2 were less frequently transplanted, but experienced post-transplant complications less often. This study revealed that high BMI is a prevalent issue in the Canadian CKD population and may influence kidney outcomes and transplant candidacy. This data will help inform clinical trials to create and study weight loss interventions for those with CKD and obesity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".