Nutritional and non-nutritional strategies in bodybuilding: Impact on kidney function
Bibliographic record
Abstract
Bodybuilders routinely engage in many dietary and other practices purported to be harmful to kidney health. Thus, a structured narrative review of the literature was performed. The search strategy and parameters identifying areas of importance closely followed the PRISMA statement guidelines. This resulted in 13 relevant full-text articles consisting of 8 case reports and 5 case series, published since 2000. Kidney disease was described in 75 bodybuilders, with diagnoses ranging from acute kidney injury (AKI), acute tubular necrosis , focal segmental glomerular sclerosis (FSGS), nephrocalcinosis, acute interstitial nephritis , nephrosclerosis, chronic interstitial nephritis, an assortment of other glomerulonephritides. The development of AKI, FSGS and nephrocalcinosis may be particular risks. There is little evidence that high-protein diets and moderate creatine supplementation pose risks to individuals with normal kidney function though long-term high protein intake in those with underlying impairment of kidney function is inadvisable. The links between anabolic androgenic steroid use and FSGS are stronger, and there are undoubted dangers of nephrocalcinosis in those taking high doses of vitamins A, D and E. Dehydrating practices, including diuretic misuse, and NSAID use also carry potential risks. It is difficult to predict the effects of multiple practices carried out in concert. Investigations into subclinical kidney damage associated with these practices have rarely been undertaken. Future research is warranted to identify the clinical and subclinical harm associated with individual practices and combinations to enable appropriate and timely advice.
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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".