Evidence map›Paper›PMID 41356835›Full record

ArticleFrontiers in nutrition2025

Impact of inflammatory and nutritional parameters on mortality in cardiovascular multimorbidity: a comprehensive prognostic analysis based on two datasets.

Ziqi Chen, Aijing Zhu, Xu Zhu, Qiang Qu, Yang Ying, Sitong Chen, Haifeng Zhang, Iokfai Cheang, Xinli Li

Abstract read
In one paragraph

Article in Frontiers in nutrition, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Ziqi Chen *State Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Aijing Zhu *Department of Gastroenterology, Zhongda Hospital, School of Medicine, Southeast University, Nanjing, China.
Xu ZhuState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Qiang QuState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Yang YingState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Sitong ChenState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Haifeng ZhangState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Iokfai CheangState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.
Xinli LiState Key Laboratory for Innovation and Transformation of Luobing Theory, Department of Cardiology, the First Affiliated Hospital with Nanjing Medical University, Jiangsu Province Hospital, Nanjing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Cardiovascular multimorbidity (CMM), defined as the coexistence of multiple cardiometabolic diseases, has posed an escalating global health burden associated with premature mortality. Systemic inflammation has been increasingly recognized as a central mechanism linking cardiometabolic diseases, yet the prognostic implications of routine inflammatory and nutritional biomarkers in patients with CMM remained unclear. Methods: This cohort study analyzed 1,928 CMM patients from the National Health and Nutrition Examination Survey (NHANES) and 364 patients from a Chinese cohort (Gaoyou). Ten inflammatory and nutritional parameters were evaluated. Associations with all-cause and cardiovascular mortality were assessed using multivariable Cox regression and restricted cubic splines. Feature selection (SHAP, Boruta, and Lasso) was employed to identify optimal predictors, followed by construction and validation of nomogram and machine learning (ML) models. Results: The systemic inflammation response index (SIRI) emerged as the strongest independent predictor of mortality. Patients in the highest SIRI quartile exhibited significantly increased risks of all-cause mortality (HR = 2.34, 95% CI: 1.88-2.90) and cardiovascular mortality (HR = 2.09, 95% CI: 1.47-2.98), with consistent performance across various subgroups. Nomograms incorporating SIRI demonstrated excellent discrimination (AUCs > 0.7) and clinical utility. Among the ML models, XGBoost achieved the highest predictive efficiency at 60, 120, and 150 months. Conclusion: SIRI, reflecting the combined influence of inflammatory responses and nutritional status, provided an available and independent biomarker for mortality risk stratification in CMM patients. The validated nomograms and web-based prediction tool offered clinicians a practical approach for individualized prognosis and informed future strategies targeting systemic inflammation and nutrition in multimorbidity management.

Indexed as

cardiovascular multimorbidity (CMM)machine learning (ML)nomogram predictionrisk stratificationsystemic inflammation response index (SIRI)

Identifiers

PMID41356835
PMCPMC12679892

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.