Trace Elements and Chronic Kidney Disease: A Cross-Sectional Study from Jamaica
Bibliographic record
Abstract
Background: Several environmental studies have reported that low-level exposure to nephrotoxic elements increases the risk of chronic kidney disease (CKD). In developing countries, finite resources can limit epidemiological studies and environmental risk assessment; however, the unique soil profile in Jamaica has raised some concerns for the potential exposure to populations who are of high risk. Method: This study investigated the potential for using trace element profiling in CKD, by analysing blood concentration levels of vanadium, chromium, iron, cobalt, copper, zinc, selenium, strontium (Sr), arsenic, barium, cadmium, mercury, and lead. Trace element analysis was conducted using inductively coupled plasma mass spectrometry. Results: One hundred and fifty-eight individuals were included and were predominantly of African descent (98%) and their ages ranged from 21 to 90 years old. Three main correlation clusters were evident: firstly, vanadium, chromium, copper, silicon, and selenium, with mercury and barium more distantly related; secondly, lead, arsenic, nickel, and Sr; and thirdly, iron and zinc. Cadmium was an outlier. Blood Sr was strongly associated with estimated glomerular filtration rate (r = -0.83; p<0.001) and strong linear progression models (r2=0.96; p<0.001). Algorithmic models placed Sr as the highest-ranking trace element biomarker (area under the curve: 95.6%; p<0.001). Discussion: The decline in kidney function may result in the retention of non-essential trace elements. Strong corresponding trends between kidney function and blood Sr concentration indicate biomarker potential for a trace element with a unique profile in patients with CKD. Other significant relationships may also be unveiled as CKD biomarkers as trace element profiling is explored in the region.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".