Evidence map›Paper›PMID 36258194›Full record

ArticleCardiovascular diabetology2022

TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy.

Benedetta Salvatori, Tina Linder, Daniel Eppel, Micaela Morettini, Laura Burattini, Christian Göbl, Andrea Tura

Open access · goldAbstract read
In one paragraph

Article in Cardiovascular diabetology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed
4.1field-weighted citation impact, top 5% of its field
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

19 citing papers in PubMed, 23 citations in OpenAlex.

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  3. The Double-Edged Sword of Gestational Insulin Resistance: Navigating Maternal Adaptation and Its Risks for Pregnancy and Offspring Health.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026
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  13. Correlation between logFrontiers in endocrinology · 2025
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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

7 authors at 3 institutions in 2 countries.

Benedetta SalvatoriCNR Institute of Neuroscience, Corso Stati Uniti 4, 35127, Padua, Italy.
Tina LinderDepartment of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria.
Daniel EppelDepartment of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria.
Micaela MorettiniDepartment of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy.
Laura BurattiniDepartment of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy.
Christian GöblDepartment of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria.
Andrea TuraCNR Institute of Neuroscience, Corso Stati Uniti 4, 35127, Padua, Italy. andrea.tura@cnr.it.
Medical University of Vienna · ATMarche Polytechnic University · ITNeuroscience Institute · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy.

methodsAt early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition.

resultsThe relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R

conclusionsWe developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG.

Indexed as

Insulin ResistanceBiomarkersBlood GlucoseC-PeptideFemaleGlucoseHumansInsulinPregnancyTriglyceridesBiomarkersBlood GlucoseC-PeptideGlucoseInsulinTriglyceridesGestational diabetesInsulin resistanceMachine learningMathematical modellingPregnancyTriglyceride-glucose index

Identifiers

PMID36258194
PMCPMC9580191
OpenAlexW4306699188

What Socratic holds

Textmetadata
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 Socratic graph.