Evidence map›Paper›PMID 40687797›Full record

ArticleiScience2025

The association between cystatin C and hypertension risk in diabetes patients: A multi-cohort cross-sectional study.

Ye Kuang, Jia Wang, Yang Wang, Chuanmei Peng, Pei He, Yong Ji, Jinrong Tian, Yong Yuan, Lei Feng

Abstract read
In one paragraph

Article in iScience, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Ye KuangDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Jia WangDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Yang WangDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Chuanmei PengDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Pei HeDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Yong JiDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Jinrong TianDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Yong YuanDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.
Lei FengDepartment of Clinical Laboratory, Yan'an Hospital Affiliated to Kunming Medical University, No. 245 East Renmin Road, Kunming 650051, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Diabetes mellitus with hypertension (DM + HTN) markedly elevates cardiovascular risks, yet its predictors remain unclear. Analyzing 5210 DM patients from three cohorts, this study identified serum cystatin C (CysC) as an independent risk factor for DM + HTN through univariate and multivariate logistic regression. A risk prediction model incorporating CysC concentration was developed and adjusted for age, sex, race, education, body mass index, smoking status, and drinking status. The model demonstrated good predictive performance and net benefit through receiver operating characteristic curves, calibration curves, and decision curve analysis. Restricted cubic spline analysis demonstrated a nonlinear relationship between CysC levels and DM + HTN risk, with concentrations above 0.94 mg/L exhibiting elevated risk. The model's performance was further evaluated using 10 machine learning algorithms and interpreted using SHapley Additive exPlanations (SHAP). This research provides a CysC-based model to aid clinicians in early identification of high-risk individuals.

Indexed as

Cardiovascular medicinePublic health

Identifiers

PMID40687797
PMCPMC12272746

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.