Evidence map›Paper›PMID 40874812›Full record

ArticleAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2025

GLM7 - A Novel Composite Glycolipid Index Derived from Routine Health Indicators for Enhanced Diagnosis and Prediction of Multimorbidity.

Zhihua Wang, Shuo Chen, Xiaojun Feng, Xi Chen, Paul C Evans, Hans Strijdom, Yu Ding, Jianping Weng, Suowen Xu

Abstract read
In one paragraph

Article in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
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

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

10 citing papers in PubMed.

  1. Review
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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

9 authors.

Zhihua WangDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Shuo ChenDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Xiaojun FengDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Xi ChenDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Paul C EvansWilliam Harvey Research Institute, Barts and The London Faculty of Medicine and Dentistry, Queen Mary University of London, London, EC1M6BQ, United Kingdom.
Hans StrijdomCentre for Cardio-metabolic Research in Africa, Division of Medical Physiology ,Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 8000, South Africa.
Yu DingDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Jianping WengDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.
Suowen XuDepartment of Endocrinology, Centre for Leading Medicine and Advanced Technologies of IHM, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230001, China.ORCID https://orcid.org/0000-0002-5488-5217

Funding

Double First-Class Initiative Research Funds of USTC YD9110002089Innovative Research Team Program of the First Affiliated Hospital of the University of Science and Technology of China CXGG02National Natural Science Foundation of China 12411530127National Natural Science Foundation of China 82003741National Natural Science Foundation of China 82102804National Natural Science Foundation of China 82300904National Natural Science Foundation of China 82370444
6 · The paper itself

Abstract

Routine health examinations for healthy adults typically involve measurements such as height, weight, blood biochemistry, complete blood count, and urinalysis. However, the current scope of physical examinations has expanded to include numerous tests, some of which have questionable insight into underlying pathology. In this study, we analyzed 26,289 samples from the NHANES (National Health and Nutrition Examination Survey) database, along with 49 included indicators, to systematically explore the correlation between conventional indicators and various diseases. Our aim was to establish new diagnostic and predictive indicators. Initially, the top 10 diagnostic and predictive indicators for five disease categories, namely cardiovascular diseases, diabetes, liver diseases, cancer, and comorbidities, are identified, and the reliability of the routine test indicators is emphasized. Moreover, GLM7 (glycolipid metabolism 7 factors), a novel indicator integrating seven routine factors, has been developed. Restricted cubic spline (RCS) analysis and forest plot evaluations reveal its relationships and risk thresholds across diseases. An extreme gradient boosting (XGBoost) model using these factors exhibits excellent predictive performance in both the NHANES discovery and CHARLS (China Health and Retirement Longitudinal Study) validation cohorts. This study confirms conventional indicators' efficacy and introduces GLM7 as a tool for disease diagnosis/prediction, providing new insights into precise disease management.

Indexed as

GlycolipidsMultimorbidityAdultAgedCardiovascular DiseasesFemaleHumansMaleMiddle AgedNutrition SurveysReproducibility of ResultsGlycolipidsdisease diagnosis predictionGLM7machine learningroutine examination indicators

Identifiers

PMID40874812
PMCPMC12622479

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.