Evidence map›Paper›PMID 42464312›Full record

ArticleDiabetology & metabolic syndrome2026

Synergistic effect of AIP and cystatin C: a study on cardiovascular risk prediction in populations with different glycemic statuses.

Kuangyi Wang, Yufeng Jiang, Liangping Zhao, Yafeng Zhou

Abstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2026. 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

4 authors.

Kuangyi WangDepartment of Cardiology, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, 215000, Republic of China.
Yufeng JiangDepartment of Cardiology, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, 215000, Republic of China.
Liangping ZhaoDepartment of Cardiology, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, 215000, Republic of China.
Yafeng ZhouDepartment of Cardiology, Suzhou Dushu Lake Hospital, The Fourth Affiliated Hospital of Soochow University, Medical Center of Soochow University, Suzhou, Jiangsu, 215000, Republic of China. zhouyafeng73@126.com.

Funding

Clinical Medicine Expert Team (Class A) of Jinji Lake Health Talents Program of Suzhou Industrial Park SZYQTD202102National Natural Science Foundation of China 82570323the Science and Technology Development Program of Jiangsu Province-Clinical Frontier Technology BE2022754
6 · The paper itself

Abstract

backgroundCardiovascular disease (CVD) poses a major global health burden. The Atherogenic Index of Plasma (AIP) and Cystatin C are novel biomarkers reflecting lipid metabolism disorders and renal/micro-inflammatory status, respectively. Their combined indicator (AIP-Cys, the product of AIP × Cystatin C) may provide improved predictive value beyond either marker alone. This study aimed to investigate the predictive role of AIP-Cys for the risk of incident CVD across different glycemic statuses: normal glucose regulation (NGR), pre-diabetes (preDM), and diabetes mellitus (DM). MATERIALS AND

methodsThis study was based on the prospective China Health and Retirement Longitudinal Study (CHARLS) cohort. A total of 6,035 participants aged ≥ 45 years without a history of CVD at baseline (2011) were included. The primary exposure was baseline AIP-Cys (the product of AIP × Cystatin C), and the outcome was the first self-reported incident heart disease or stroke event during follow-up (until 2020). Glycemic status was defined based on fasting plasma glucose and glycated hemoglobin levels. Multivariable Cox proportional hazards models were used to estimate hazard ratios (HRs). Restricted cubic splines and piecewise linear models were applied to analyze nonlinear relationships. Mediation and sensitivity analyses were conducted to verify the robustness of the results.

resultsDuring a median follow-up of 9.0 years, 651 incident CVD cases occurred. In the fully adjusted model, AIP-Cys was independently associated with CVD risk in the overall (HR per unit increase: 1.34; 95% CI: 1.06-1.69), NGR (HR: 1.60; 95% CI: 1.13-2.26), and preDM (HR: 1.32; 95% CI: 1.02-1.70) populations, but not in the DM population. RCS analysis revealed a significant nonlinear relationship in the NGR and preDM groups (P for nonlinearity < 0.05). Piecewise regression identified inflection points (0.47 for NGR; 0.37 for preDM), below which risk increased sharply. Quartile analysis showed the highest risk in Q3 rather than Q4, suggesting a saturation effect. Mediation analysis indicated that systolic blood pressure partially mediated this association (mediating proportion: 14.6% in overall population). The predictive performance of AIP-Cys was modest for short-term risk (1-year AUC: 0.64-0.67) but limited for long-term prediction.

conclusionsThe composite indicator AIP-Cys, derived from AIP and Cystatin C, is an independent risk factor for incident CVD in individuals with NGR and preDM. The association exhibits a nonlinear threshold pattern. However, its predictive value is limited in the DM population, and its long-term predictive performance is modest. Additionally, the self-reported nature of cardiovascular outcomes represents a limitation that should be considered when interpreting these findings. By integrating lipid metabolism and renal/inflammatory pathways, this indicator offers a novel biomarker-based approach for early cardiovascular risk identification in the pre-diabetes stage. Future research should further explore its potential for clinical translation and intervention.

Indexed as

Atherogenic index of plasmaCardiovascular diseaseCHARLSCystatin CGlucose metabolic status

Identifiers

PMID42464312
PMCPMC13656459

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.