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Record W4410034833 · doi:10.1007/s11255-025-04535-w

Risk factors for coronary artery calcification in Chinese patients undergoing maintenance hemodialysis: a meta-analysis

2025· review· en· W4410034833 on OpenAlexaboutno aff
Mengjiao Li, Yuxia Li, Shuting Liu, Yan Yang, Ping Jiang

Bibliographic record

VenueInternational Urology and Nephrology · 2025
Typereview
Languageen
FieldMedicine
TopicParathyroid Disorders and Treatments
Canadian institutionsnot available
FundersShanghai Municipal Health Commission
KeywordsMedicineInternal medicineHemodialysisCoronary artery diseaseMeta-analysisOdds ratioKidney diseaseNephrologyEnd stage renal diseaseCardiologyDiabetes mellitusEndocrinology

Abstract

fetched live from OpenAlex

BACKGROUND: Chronic kidney disease (CKD) progression to end-stage renal disease (ESRD) increases cardiovascular disease (CVD) risk, with coronary artery calcification (CAC) affecting 70% of Chinese maintenance hemodialysis (MHD) patients. Prolonged MHD treatment is linked to calcium-phosphorus imbalance and accelerated CAC progression. However, conflicting findings on CAC risk factors persist due to methodological heterogeneity in existing studies. OBJECTIVE: To systematically analyze the risk factors for coronary artery calcification in patients undergoing maintenance hemodialysis in China. METHODS: We conducted a computer-assisted search of ten databases, including PubMed, Web of Science, CBM, Wanfang, CNKI, and VIP, for observational studies (cohort, case-control, and cross-sectional studies) published from inception to October 21, 2024, on risk factors for coronary artery calcification (CAC) in Chinese patients with maintenance hemodialysis (MHD). The studies were independently screened by two investigators according to the PRISMA guidelines and diagnosed with coronary artery calcification through imaging techniques (CT). Odds ratios (ORs) and 95% confidence intervals (CIs) were used for the reported outcomes. The quality of the studies was assessed using the Newcastle-Ottawa Scale. A meta-analysis of the included data was performed using either a random effects model or a fixed effects model, we performed a meta-analysis using the Stata 17.0 software. RESULTS: The review included 24 studies with a total sample size of 2,875 patients. A meta-analysis of these 24 studies (n = 2875) found that diabetes mellitus (OR = 2.32, 95% CI 1.37-3.27) and elevated iPTH (OR = 1.59, 95% CI 1.21-1.96) were the strongest predictors of an increased risk of coronary artery calcification (CAC) in Chinese maintenance hemodialysis (MHD) patients. Additionally, elevated high-sensitivity C-reactive protein (hs-CRP) levels, elevated serum calcium, advanced age, longer duration of dialysis treatment, elevated serum phosphorus, and elevated sclerostin (SOST) levels were significant predictors of an increased risk of concurrent CAC in Chinese MHD patients. However, the correlation between hypertension, serum magnesium, fibroblast growth factor-23 (FGF-23), alkaline phosphatase (ALP), and MHD with concomitant CAC was not significant, likely due to the wide variation in sample size and study type. CONCLUSION: Diabetes mellitus and elevated iPTH are the most significant clinical risk factors for coronary artery calcification (CAC) in Chinese maintenance hemodialysis (MHD) patients, and healthcare professionals should prioritize this population. Additionally, monitoring calcium and phosphorus metabolism, along with inflammatory markers (e.g., hs-CRP, SOST), can further reduce the risk of cardiovascular disease. Early health education and targeted, individualized treatment strategies for high-risk groups are recommended to prevent and delay the onset and progression of CAC in MHD patients.

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)0.007
metaresearch head score (Gemma)0.012
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 score0.013
Threshold uncertainty score0.038

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.012
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0130.035
Bibliometrics0.0050.006
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
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 head0.040
GPT teacher head0.345
Teacher spread0.306 · 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

Citations3
Published2025
Admission routes1
Has abstractyes

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