Evidence mapPaperPMID 40317034Full record

ArticleCardiovascular diabetology2025

Integrating adipsin with novel cardiometabolic and inflammatory indices for enhanced early prediction of gestational diabetes mellitus: a prospective cohort study.

Meizhi Cai, Xuan Jiang, Xinyi Xu, Sidi Zhao, Yue Sun, Yushuo Yang, Ping Yang, Chen Fang, Yifan Huang

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Article in Cardiovascular diabetology, 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

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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.

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4 · The record

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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.

Meizhi Cai *Clinical Nutrition Division, The Second Affiliated Hospital of Soochow University, No.1055 San Xiang Road, Suzhou, 215000, Jiangsu, China. caimeizhi1987@hotmail.com.
Xuan Jiang *School of Public Health, Suzhou Medical College of Soochow University, No.199 Ren Ai Road, Suzhou, 215000, Jiangsu, China.
Xinyi Xu *School of Public Health, Suzhou Medical College of Soochow University, No.199 Ren Ai Road, Suzhou, 215000, Jiangsu, China.
Sidi ZhaoClinical Nutrition Division, The Second Affiliated Hospital of Soochow University, No.1055 San Xiang Road, Suzhou, 215000, Jiangsu, China.
Yue SunSchool of Public Health, Suzhou Medical College of Soochow University, No.199 Ren Ai Road, Suzhou, 215000, Jiangsu, China.
Yushuo YangSchool of Public Health, Suzhou Medical College of Soochow University, No.199 Ren Ai Road, Suzhou, 215000, Jiangsu, China.
Ping YangClinical Nutrition Division, The Second Affiliated Hospital of Soochow University, No.1055 San Xiang Road, Suzhou, 215000, Jiangsu, China.
Chen FangClinical Nutrition Division, The Second Affiliated Hospital of Soochow University, No.1055 San Xiang Road, Suzhou, 215000, Jiangsu, China.
Yifan HuangSchool of Public Health, Suzhou Medical College of Soochow University, No.199 Ren Ai Road, Suzhou, 215000, Jiangsu, China. yyzhyf@hotmail.com.

Funding

Soochow University KY2024266BSuzhou Municipal Health Commission KJXW2021011Suzhou Municipal Science and Technology Bureau SKY2023119
6 · The paper itself

Abstract

backgroundEarly identification of individuals at risk for gestational diabetes mellitus (GDM) is essential for mitigating its adverse effects on both maternal and foetal health. This study aimed to evaluate the predictive value of the cardiometabolic index (CMI), systemic inflammation response index (SIRI), and serum adipsin levels for GDM.

methodsA total of 1660 pregnant women were enrolled in this study conducted in Suzhou, China. Baseline clinical data, including blood glucose levels, lipid profiles, and blood cell counts, were collected at 12 weeks of gestation. GDM was diagnosed between 24 and 28 weeks of gestation. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to assess the associations and predictive performance of CMI, SIRI, and adipsin for GDM.

resultsCompared with non-GDM participants, those with GDM exhibited significantly higher CMI and SIRI values and lower serum adipsin levels at baseline. Increased CMI and SIRI, as well as reduced adipsin levels, were independently associated with a higher risk of GDM in both unadjusted and adjusted models (all P < 0.05). The composite model incorporating all three biomarkers achieved a higher area under the curve (AUC) of 0.918 compared with the individual models for CMI (AUC = 0.825), SIRI (AUC = 0.802), and adipsin (AUC = 0.724).

conclusionsCMI, SIRI, and serum adipsin are independently associated with GDM risk, and their combination provides a promising multi-biomarker strategy for early GDM prediction. Further studies are needed to validate these findings in diverse populations.

Indexed as

Complement Factor DDiabetes, GestationalInflammationInflammation MediatorsAdultBiomarkersBlood GlucoseCardiometabolic Risk FactorsChinaEarly DiagnosisFemaleGestational AgeHumansPredictive Value of TestsPregnancyPrognosisBiomarkersBlood GlucoseCFD protein, humanComplement Factor DInflammation MediatorsAdipokinesAdipsinGlucose intoleranceInflammationInsulin resistanceMetabolic abnormalitiesPregnancy-Diabetes

Identifiers

PMID40317034
PMCPMC12048925

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.