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Record W3081618755 · doi:10.1681/asn.2020030384

Modifiable Lifestyle Factors for Primary Prevention of CKD: A Systematic Review and Meta-Analysis

2020· review· en· W3081618755 on OpenAlexaff
Jaimon T. Kelly, Guobin Su, Zhang La, Xindong Qin, Skye Marshall, Ailema González-Ortíz, Catherine M. Clase, Katrina L. Campbell, Hong Xu, Juan Jesús Carrero

Bibliographic record

VenueJournal of the American Society of Nephrology · 2020
Typereview
Languageen
FieldMedicine
TopicChronic Kidney Disease and Diabetes
Canadian institutionsMcMaster UniversityImpact
FundersEuropean Renal Association-European Dialysis and Transplant AssociationConsejo Nacional de Ciencia y TecnologíaVetenskapsrådetGuangdong Provincial Hospital of Traditional Chinese Medicine
KeywordsMeta-analysisPrimary preventionMedicineGerontologyEnvironmental healthInternal medicineDisease

Abstract

fetched live from OpenAlex

Significance Statement Although CKD incidence is increasing, no evidence-based lifestyle recommendations for CKD primary prevention apparently exist. To evaluate evidence associating modifiable lifestyle factors and incidence of CKD, the authors undertook a systematic review and meta-analysis. Their analysis, which included 104 observational studies of 2,755,719 participants, demonstrated consistency of evidence for a number of measures associated with preventing CKD onset, including increasing dietary intake of vegetables and potassium (21% reduced odds and 22% reduced odds, respectively), increasing physical activity levels (18% reduced odds), moderating alcohol consumption (15% reduced risk), lowering sodium intake (21% increased odds), and stopping tobacco smoking (18% increased risk). In the absence of clinical trial evidence, these findings can help inform public health recommendations and patient-centered discussions in clinical practice about lifestyle measures to prevent CKD. Background Despite increasing incidence of CKD, no evidence-based lifestyle recommendations for CKD primary prevention apparently exist. Methods To evaluate the consistency of evidence associating modifiable lifestyle factors and CKD incidence, we searched MEDLINE, Embase, CINAHL, and references from eligible studies from database inception through June 2019. We included cohort studies of adults without CKD at baseline that reported lifestyle exposures (diet, physical activity, alcohol consumption, and tobacco smoking). The primary outcome was incident CKD (eGFR<60 ml/min per 1.73 m 2 ). Secondary outcomes included other CKD surrogate measures (RRT, GFR decline, and albuminuria). Results We identified 104 studies of 2,755,719 participants with generally a low risk of bias. Higher dietary potassium intake associated with significantly decreased odds of CKD (odds ratio [OR], 0.78; 95% confidence interval [95% CI], 0.65 to 0.94), as did higher vegetable intake (OR, 0.79; 95% CI, 0.70 to 0.90); higher salt intake associated with significantly increased odds of CKD (OR, 1.21; 95% CI, 1.06 to 1.38). Being physically active versus sedentary associated with lower odds of CKD (OR, 0.82; 95% CI, 0.69 to 0.98). Current and former smokers had significantly increased odds of CKD compared with never smokers (OR, 1.18; 95% CI, 1.10 to 1.27). Compared with no consumption, moderate consumption of alcohol associated with reduced risk of CKD (relative risk, 0.86; 95% CI, 0.79 to 0.93). These associations were consistent, but evidence was predominantly of low to very low certainty. Results for secondary outcomes were consistent with the primary finding. Conclusions These findings identify modifiable lifestyle factors that consistently predict the incidence of CKD in the community and may inform both public health recommendations and clinical practice.

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 imitation

Not 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.

metaresearch head score (Codex)0.020
metaresearch head score (Gemma)0.046
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.021
Threshold uncertainty score0.105

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0200.046
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0210.034
Bibliometrics0.0100.010
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.063
GPT teacher head0.355
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreReview

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".

Quick stats

Citations316
Published2020
Admission routes1
Has abstractyes

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