Evidence map›Paper›PMID 41419970›Full record

ArticleDiabetology & metabolic syndrome2025

Associations of inflammation-related hematological profile with the early-stages of cardiovascular-kidney-metabolic syndrome and the mediating role of body composition: evidence from the China National Health Survey.

Lunhui Huang, Binbin Lin, Yueyi Mu, Yansong Ren, Qiang Li, Yong Li, Yueshen Ma, Yulong Fan, Guoqing Zhu, Zhen Song and 1 more

Abstract read
In one paragraph

Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Lunhui Huang *State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Binbin Lin *State Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yueyi MuState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yansong RenState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Qiang LiState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yong LiState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yueshen MaState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Yulong FanState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China.
Guoqing ZhuState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. zhuguoqing@ihcams.ac.cn.
Zhen SongState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. songzhen@ihcams.ac.cn.
Yonghui XiaState Key Laboratory of Experimental Hematology, Haihe Laboratory of Cell Ecosystem, Institute of Hematology & Blood Diseases Hospital, National Clinical Research Center for Blood Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Tianjin, China. xiayonghui@ihcams.ac.cn.

Funding

CAMS Innovation Fund for Medical Sciences 2021-I2M-1-023
6 · The paper itself

Abstract

backgroundChronic low-grade inflammation is increasingly recognized as a pivotal driver in the development of early-stage cardiovascular-kidney-metabolic (CKM) syndrome. Nonetheless, the complex interplay among inflammation-associated hematological indices, body composition, and CKM risk remains inadequately understood.

methodsIn this cross-sectional study, data from 5,692 participants of the China National Health Survey (CNHS) were analyzed. We employed advanced machine learning techniques-including Random Forest, Least Absolute Shrinkage and Selection Operator​(LASSO) regression, and eXtreme Gradient Boosting (XGBoost)-in conjunction with traditional epidemiological methods to assess the predictive value of an inflammation-related hematological profile for early-stage CKM. Restricted cubic spline regression was used to explore nonlinear dose-response relationships, and generalized structural equation modeling investigated the mediating role of body composition in the inflammation-CKM pathway.

resultsSix biomarkers-Neutrophil-to-HDL ratio (NHR), Monocyte-to-HDL ratio (MHR), High-sensitivity C-reactive protein/albumin ratio (CAR), High-fluorescence reticulocyte fraction (HFR), Reticulocyte production index (RPI), and Reticulocyte count (RET#)-were consistently prioritized across models. Participants in the highest quartiles of NHR, MHR, and RET# exhibited markedly elevated odds of early-stages of CKM (OR = 10.4, 7.75, and 6.99, respectively; all P for trend < 0.0001). Nonlinear analyses revealed critical thresholds-specifically, NHR > 7.05 and MHR > 0.66-beyond which early-stages of CKM risk escalated steeply. Mediation analyses indicated that imbalances in body composition, particularly increased adiposity and reduced muscle mass, accounted for 20-57% of the association between systemic inflammation and early-stage of CKM syndrome. Subgroup analyses further underscored that the predictive impact of reticulocyte parameters was amplified in smokers and individuals aged < 60 years.

conclusionThis study validates NHR and MHR as robust, clinically actionable biomarkers for early CKM screening. The delineated nonlinear thresholds and the mediating effects of body composition provide a strategic framework for targeted interventions-prioritizing anti-inflammatory treatments in high-risk groups (e.g., smokers with RET# >143.03 × 10³/µL) and muscle-preserving therapies to mitigate sarcopenic adiposity.

Indexed as

Body composition mediationCardiovascular-kidney-metabolic (CKM) syndromeInflammation-related hematological profileMachine learningThreshold effects

Identifiers

PMID41419970
PMCPMC12831305

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