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Record W7117538309 · doi:10.1136/bmjopen-2025-107831

Risk factors for hyperuricaemia–hypertension comorbidity in Chinese children and adolescents: a protocol for systematic review and meta-analysis

2025· article· en· W7117538309 on OpenAlexaboutno aff
Yang Jing, Yuefeng Yang, Wengang Xia, Cong-Cong Yu

Bibliographic record

VenueBMJ Open · 2025
Typearticle
Languageen
FieldMedicine
TopicGout, Hyperuricemia, Uric Acid
Canadian institutionsnot available
Fundersnot available
KeywordsProtocol (science)ComorbidityEpidemiologyMEDLINEPublic healthRisk factor

Abstract

fetched live from OpenAlex

INTRODUCTION: Hypertension (HTN) and hyperuricaemia (HUA) are chronic metabolic disorders that frequently coexist. Research indicates that HUA prevalence among adolescents and children in mainland China is significantly higher than the global average, with a continuing upward trend. When these conditions occur concomitantly, their detrimental effects on the cardiovascular and cerebrovascular systems far exceed those of either condition alone. The comorbidity of HUA and HTN markedly elevates the risk of adverse cardiovascular and cerebrovascular events, including stroke, ischaemic heart disease, myocardial hypertrophy and left ventricular diastolic dysfunction. In adolescent populations, this comorbidity may also induce subclinical damage such as microangiopathy, premature arteriosclerosis and declined glomerular filtration function, thereby increasing the likelihood of chronic cardiorenal diseases in adulthood. Current research on HUA+HTN comorbidity among Chinese children and adolescents is predominantly limited by regional constraints, small sample sizes or inconsistent diagnostic criteria. This hinders the development of precise prevention strategies and delays timely interventions. Thus, a systematic evaluation of the risk factors for HUA+HTN comorbidity in this population is urgently needed. Such evaluation would provide evidence-based data to facilitate early detection of high-risk individuals and guide tailored preventive interventions. It would also lay the groundwork for reducing the future burden of chronic conditions such as stroke and heart failure. METHODS AND ANALYSIS: Electronic databases (Cochrane Library, PubMed, Web of Science, EMBASE, CNKI, CBM, VIP, Wanfang) will be systematically searched up to 1 June 2025, with no language restrictions applied. This will identify observational studies (cohort, case-control and cross-sectional designs) investigating risk factors for HTN and HUA in Chinese children and adolescents. In addition, randomised controlled trials (RCTs) that report baseline or preintervention data relevant to HTN+HUA comorbidity risk factors will also be considered for inclusion, where such data can provide supplementary evidence on associations between exposures and outcomes. Methodological quality will be evaluated via tools appropriate for each study design (Agency for Healthcare Research and Quality checklist for cross-sectional studies; Newcastle-Ottawa Scale for case-control and cohort studies). The risk of bias in RCTs will be evaluated using the RoB 2. All analyses will be conducted per the Cochrane Handbook recommendations for observational studies. ETHICS AND DISSEMINATION: This systematic review protocol will systematically evaluate the risk factors for HUA+HTN comorbidity in Chinese children and adolescents. We will analyse secondary data, and this does not require ethics approval. The findings will be published in peer-reviewed journals, at relevant conferences and will be shared in plain language in social media. Moreover, the findings of this review could guide the direction of healthcare practice and research. PROSPERO REGISTRATION NUMBER: CRD42024613929.

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)0.025
metaresearch head score (Gemma)0.041
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: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.034
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.041
Meta-epidemiology (narrow)0.0040.003
Meta-epidemiology (broad)0.0260.030
Bibliometrics0.0100.011
Science and technology studies0.0020.002
Scholarly communication0.0050.005
Open science0.0040.003
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0340.002

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 head0.098
GPT teacher head0.435
Teacher spread0.337 · 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
GenreProtocol

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

Citations0
Published2025
Admission routes1
Has abstractyes

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