The general external exposome and the development or progression of chronic kidney disease: A systematic review and meta-analyses
Bibliographic record
Abstract
The impact of environmental risk factors on chronic kidney disease (CKD) remains unclear. This systematic review aims to provide an overview of the literature on the association between the general external exposome and CKD development or progression. We searched MEDLINE and EMBASE for case-control or cohort studies, that investigated the association of the general external exposome with a change in eGFR or albuminuria, diagnosis or progression of CKD, or CKD-related mortality. The risk of bias of included studies was assessed using the Newcastle-Ottawa Scale. Summary effect estimates were calculated using random-effects meta-analyses. Most of the 66 included studies focused on air pollution (n = 33), e.g. particulate matter (PM) and nitric oxides (NOx), and heavy metals (n = 21) e.g. lead and cadmium. Few studies investigated chemicals (n = 7) or built environmental factors (n = 5). No articles on other environment factors such as noise, food supply, or urbanization were found. PM2.5 exposure was associated with an increased CKD and end-stage kidney disease incidence, but not with CKD-related mortality. There was mixed evidence regarding the association of NO2 and PM10 on CKD incidence. Exposure to heavy metals might be associated with an increased risk of adverse kidney outcomes, however, evidence was inconsistent. Studies on effects of chemicals or built environment on kidney outcomes were inconclusive. In conclusion, prolonged exposure to PM2.5 is associated with an increased risk of CKD incidence and progression to kidney failure. Current studies predominantly investigate the exposure to air pollution and heavy metals, whereas chemicals and the built environment remains understudied. Substantial heterogeneity and mixed evidence were found across studies. Therefore, long-term high-quality studies are needed to elucidate the impact of exposure to chemicals or other (built) environmental factors and CKD.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".