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Record W2093413740 · doi:10.1371/journal.pone.0114259

Hyperuricemia and Risk of Incident Hypertension: A Systematic Review and Meta-Analysis of Observational Studies

2014· review· en· W2093413740 on OpenAlexaboutno aff
Ji Wang, Tianqiang Qin, Jianrong Chen, Yulin Li, Ling Wang, He Huang, Jing Li

Bibliographic record

VenuePLoS ONE · 2014
Typereview
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineHyperuricemiaInternal medicineMeta-analysisConfoundingRelative riskOdds ratioCohort studyObservational studyIncidence (geometry)PopulationConfidence intervalUric acidEnvironmental health

Abstract

fetched live from OpenAlex

BACKGROUND: Observational studies of the relationship between hyperuricemia and the incidence of hypertension are controversial. We conducted a systematic review and meta-analysis to assess the association and consistency between uric acid levels and the risk of hypertension development. METHODS: We searched MEDLINE, EMBASE, CBM (Chinese Biomedicine Database) through September 2013 and reference lists of retrieved studies to identify cohort studies and nested case-control studies with uric acid levels as exposure and incident hypertension as outcome variables. Two reviewers independently extracted data and assessed study quality using Newcastle-Ottawa Scale. Extracted information included study design, population, definition of hyperuricemia and hypertension, number of incident hypertension, effect sizes, and adjusted confounders. Pooled relative risks (RRs) and corresponding 95% confidence intervals (CIs) for the association between hyperuricemia and risk of hypertension were calculated using a random-effects model. RESULTS: We included 25 studies with 97,824 participants assessing the association between uric acid and incident hypertension in our meta-analysis. The quality of included studies is moderate to high. Random-effects meta-analysis showed that hyperuricemia was associated with a higher risk of incident hypertension, regardless of whether the effect size was adjusted or not, whether the data were categorical or continuous as 1 SD/1 mg/dl increase in uric acid level (unadjusted: RR = 1.73, 95% CI 1.46∼2.06 for categorical data, RR = 1.22, 95% CI 1.03∼1.45 for a 1 SD increase; adjusted: RR = 1.48, 95% CI 1.33∼1.65 for categorical data, RR = 1.15, 95% CI 1.06∼1.26 for a 1 mg/dl increase), and the risk is consistent in subgroup analyses and have a dose-response relationship. CONCLUSIONS: Hyperuricemia may modestly increase the risk of hypertension incidence, consistent with a dose-response relationship.

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)0.024
metaresearch head score (Gemma)0.056
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 score0.024
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.056
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0200.038
Bibliometrics0.0090.011
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.406
GPT teacher head0.372
Teacher spread0.034 · 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

Citations339
Published2014
Admission routes1
Has abstractyes

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Same venuePLoS ONESame topicGout, Hyperuricemia, Uric AcidFrench-language works237,207