Evidence mapPaperPMID 40856552Full record

ArticleAnnals of medicine2025

Integrated machine learning and population attributable fraction analysis of systemic inflammatory indices for mortality risk prediction in diabetes and prediabetes.

Zixi Zhang, Chenyang Li, Yichao Xiao, Chan Liu, Xiaoqin Luo, Cancan Wang, Yongguo Dai, Qiuzhen Lin, Zeying Zhang, Cheng Zheng and 3 more

Abstract read
In one paragraph

Article in Annals of medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing 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

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

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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

13 authors.

Zixi ZhangDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Chenyang LiDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, People's Republic of China.
Yichao XiaoDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Chan LiuDepartment of International Medicine, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Xiaoqin LuoDepartment of Geriatrics, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Cancan WangDepartment of Endocrinology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Yongguo DaiDepartment of Pharmacy, Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Qiuzhen LinDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Zeying ZhangDepartment of Cardiology, Xiamen Cardiovascular Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian Province, People's Republic of China.
Cheng ZhengDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, People's Republic of China.
Jiafeng LinDepartment of Cardiology, The Second Affiliated Hospital, Wenzhou Medical University, Wenzhou, Zhejiang Province, People's Republic of China.
Tao TuDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.
Qiming LiuDepartment of Cardiology, The Second Xiangya Hospital, Central South University, Changsha, Hunan Province, People's Republic of China.ORCID 0000-0002-5213-2668

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundChronic systemic inflammation is a key contributor to cardiometabolic complications in diabetes mellitus (DM) and prediabetes (PreDM). Composite inflammatory indices-including neutrophil-to-lymphocyte ratio (NLR), monocyte-to-lymphocyte ratio (MLR), systemic inflammation response index (SIRI), systemic immune-inflammation index (SII), platelet-to-hemoglobin ratio (PHR), and aggregate inflammation systemic index (AISI)-have shown prognostic value for mortality. However, their integrated assessment using machine learning and quantification at the population level remain limited.

methodsIn this retrospective cohort study, 11,304 adults with DM or PreDM from the National Health and Nutrition Examination Survey (NHANES, 2005-2018) were analyzed. The primary outcomes were all-cause and cardiovascular mortality. Associations between inflammatory indices and mortality were evaluated using Cox proportional hazards models. Predictive performance was assessed via Extreme Gradient Boosting (XGBoost), and population attributable fractions (PAFs) estimated the mortality burden related to systemic inflammation.

resultsNLR, MLR, SIRI, SII, and AISI were independently associated with all-cause and cardiovascular mortality. MLR showed the strongest association (HR: 2.948 and 3.717 for all-cause and CVD mortality, respectively). XGBoost identified SIRI, SII, AISI, MLR, and NLR as key predictors, with SIRI ranked highest for cardiovascular mortality. Inclusion of inflammatory indices improved model discrimination and calibration. PAF analysis suggested that 10-20% of mortality reduction could be attributed to improved inflammatory profiles.

conclusionSystemic inflammatory indices are independent predictors of mortality in individuals with DM or PreDM. Their integration into machine learning models enhances risk prediction and may inform population-level strategies for cardiometabolic risk stratification.

Indexed as

Cardiovascular DiseasesDiabetes MellitusInflammationMachine LearningPrediabetic StateAdultAgedFemaleHumansMaleMiddle AgedMonocytesNeutrophilsNutrition SurveysPrognosisProportional Hazards ModelsDiabetes mellitusmortalitynational health and nutrition examination surveypopulation attributable fractionprediabetessystemic inflammatory indices

Identifiers

PMID40856552
PMCPMC12302434

What Socratic holds

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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 Socratic graph.