Definition and research guide
This page combines a MeSH definition, publication statistics and selected research abstracts. It is a starting point for reading the literature, not a medical textbook or a systematic evidence review.
Content available: 4 complete abstracts (747 words), 10 references. Full article text is not stored or reproduced here; follow the PMID, DOI or PMC links to read the original sources and their access terms.
Definition
A test to determine the ability of an individual to maintain homeostasis of blood glucose. It includes measuring blood glucose levels in a fasting state, and at prescribed intervals before and after oral glucose intake (75 or 100 g) or intravenous infusion (0.5 g/kg).
Definition: NLM Medical Subject Headings (MeSH) scope note (Public domain (U.S. National Library of Medicine); attribution requested). View the MeSH record.
Also known as: Glucose Tolerance Tests; Intravenous Glucose Tolerance Test; Intravenous Glucose Tolerance; Oral Glucose Tolerance Test; OGTT; Oral Glucose Tolerance; Glucose Tolerance, Oral.
Where it fits in the MeSH classification
- Broader topics
- Blood Chemical Analysis; Diagnostic Techniques, Endocrine
Related concepts
- Homeostasis
- Blood Glucose
Research landscape
PubMed indexes 38,461 records tagged with the MeSH heading “Glucose Tolerance Test” (counts retrieved 2026-11-04). 3,513 of them (9.1%) were published from 2020 onward, and the busiest period so far is through 1999 with 17,120 records.
Reviews account for 1,529 records (4.0%) and meta-analyses for 111. Randomized controlled trials number 1,879 (4.9%) as a share of all records on the topic.
Show the data
| Period | Records |
|---|---|
| through 1999 | 17,120 |
| 2000-2009 | 7,611 |
| 2010-2019 | 10,217 |
| 2020 onward | 3,513 |
Publication-type chart data
| Type | Records | Share |
|---|---|---|
| Reviews | 1,529 | 4.0% |
| Meta-analyses | 111 | 0.3% |
| Randomized controlled trials | 1,879 | 4.9% |
Method: PubMed query "Glucose Tolerance Test"[MeSH Terms] with publication-type and date filters, retrieved 2026-11-04.
Reading the evidence in diagnostics and medical technology
A test or device is judged by how well it detects a condition, how often it gives false alarms or misses cases, and whether using it improves outcomes for people. Accuracy depends on who is tested and how common the condition is, so a test that performs well in a specialist hospital can perform differently in the community. Check the reference standard, the population studied, the sensitivity and specificity and whether results were confirmed in a separate group.
Evidence from open-access studies
These open-access studies come from PubMed’s best-match results for this heading (papers whose main topic it is), ranked here by citation count, with one recent addition. Each abstract is reproduced in full under its recorded open licence so that you can read the authors’ own words. They illustrate the literature; they are not a systematic summary of it. Citation counts measure attention, not reliability or clinical benefit.
| Study / authors | Year | Source label / design | Europe PMC citations | Licence / source |
|---|---|---|---|---|
| Selection of the appropriate method for the assessment of insulin resistance | 2011 | Review article Design not recorded in structured metadata | 190 | cc by PMID 22112229 · DOI · PMC3258205 |
| Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study | 2019 | Research article Design not recorded in structured metadata | 70 | cc by PMID 31779625 · DOI · PMC6883544 |
| Uric Acid Produces an Inflammatory Response through Activation of NF-κB in the Hypothalamus: Implications for the Pathogenesis of Metabolic Disorders | 2015 | Research article Design not recorded in structured metadata | 66 | cc by PMID 26179594 · DOI · PMC4503982 |
| Revisited guidelines for metabolic tolerance tests in mice | 2025 | Review article Design not recorded in structured metadata | 31 | cc by PMID 39587363 · DOI · PMC11695259 |
Selection of the appropriate method for the assessment of insulin resistance [1]
Abstract reading summary — source excerpts
- Opening excerpt
- Insulin resistance is one of the major aggravating factors for metabolic syndrome.
- Closing excerpt
- This review therefore provides guidelines and advices which must be considered before proceeding with a study.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (148 words)
Insulin resistance is one of the major aggravating factors for metabolic syndrome. There are many methods available for estimation of insulin resistance which range from complex techniques down to simple indices. For all methods of assessing insulin resistance it is essential that their validity and reliability is established before using them as investigations. The reference techniques of hyperinsulinaemic euglycaemic clamp and its alternative the frequently sampled intravenous glucose tolerance test are the most reliable methods available for estimating insulin resistance. However, many simple methods, from which indices can be derived, have been assessed and validated e.g. homeostasis model assessment (HOMA), quantitative insulin sensitivity check index (QUICKI). Given the increasing number of simple indices of IR it may be difficult for clinicians and researchers to select the most appropriate index for their studies. This review therefore provides guidelines and advices which must be considered before proceeding with a study.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 22112229 · DOI · PMC3258205
Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study [2]
Abstract reading summary — source excerpts
- Opening excerpt
- BackgroundInsulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions.
- Closing excerpt
- The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (313 words)
BackgroundInsulin resistance (IR) is predictive for type 2 diabetes and associated with various metabolic abnormalities in fasting conditions. However, limited data are available on how IR affects metabolic responses in a non-fasting setting, yet this is the state people are mostly exposed to during waking hours in the modern society. Here, we aim to comprehensively characterise the metabolic changes in response to an oral glucose test (OGTT) and assess the associations of these changes with IR.MethodsBlood samples were obtained at 0 (fasting baseline, right before glucose ingestion), 30, 60, and 120 min during the OGTT. Seventy-eight metabolic measures were analysed at each time point for a discovery cohort of 4745 middle-aged Finnish individuals and a replication cohort of 595 senior Finnish participants. We assessed the metabolic changes in response to glucose ingestion (percentage change in relative to fasting baseline) across the four time points and further compared the response profile between five groups with different levels of IR and glucose intolerance. Further, the differences were tested for covariate adjustment, including gender, body mass index, systolic blood pressure, fasting, and 2-h glucose levels. The groups were defined as insulin sensitive with normal glucose (IS-NGT), insulin resistant with normal glucose (IR-NGT), impaired fasting glucose (IFG), impaired glucose tolerance (IGT), and new diabetes (NDM). IS-NGT and IR-NGT were defined as the first and fourth quartile of fasting insulin in NGT individuals.ResultsGlucose ingestion induced multiple metabolic responses, including increased glycolysis intermediates and decreased branched-chain amino acids, ketone bodies, glycerol, and triglycerides. The IR-NGT subgroup showed smaller responses for these measures (mean + 23%, interquartile 9-34% at 120 min) compared to IS-NGT (34%, 23-44%, P ConclusionsInsulin-resistant non-diabetic individuals are exposed to a similar adverse postprandial metabolic milieu, and analogous cardiometabolic risk, as those with type 2 diabetes. The wide range of metabolic abnormalities associated with IR highlights the necessity of diabetes diagnostics and clinical care beyond glucose management.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 31779625 · DOI · PMC6883544
Uric Acid Produces an Inflammatory Response through Activation of NF-κB in the Hypothalamus: Implications for the Pathogenesis of Metabolic Disorders [3]
Abstract reading summary — source excerpts
- Opening excerpt
- Epidemiological studies have shown that an elevated uric acid (UA) level predicts the development of metabolic syndrome and diabetes; however, there is no direct evidence of this, and the underlying mechanism remains unclear.
- Closing excerpt
- Our findings provide a potential therapeutic strategy for UA-induced metabolic disorders.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (140 words)
Epidemiological studies have shown that an elevated uric acid (UA) level predicts the development of metabolic syndrome and diabetes; however, there is no direct evidence of this, and the underlying mechanism remains unclear. Here, we showed that a high-UA diet triggered the expression of pro-inflammatory cytokines, activated the NF-κB pathway, and increased gliosis in the hypothalamus. Intracerebroventricular injection of UA induced hypothalamic inflammation and reactive gliosis, whereas these effects were markedly ameliorated by the inhibition of NF-κB. Moreover, magnetic resonance imaging confirmed that hyperuricemia in rodents and humans was associated with gliosis in the mediobasal hypothalamus. Importantly, the rats administered UA exhibited dyslipidemia and glucose intolerance, which were probably mediated by hypothalamic inflammation and hypothalamic neuroendocrine alterations. These results suggest that UA can cause hypothalamic inflammation via NF-κB signaling. Our findings provide a potential therapeutic strategy for UA-induced metabolic disorders.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 26179594 · DOI · PMC4503982
Revisited guidelines for metabolic tolerance tests in mice [4]
Abstract reading summary — source excerpts
- Opening excerpt
- Preclinical mouse models are extensively used in biomedical research to gain insight into disease mechanisms and to test new drug treatments.
- Closing excerpt
- We hope this work will help scientists establish more consensual guidelines for maximizing the relevance and clinical translation of studies using mouse models in metabolic research.
- Interpretation limits
- Verbatim excerpts from the authors’ abstract; the closing excerpt may describe a finding, limitation or recommendation. Design, population, effect size and uncertainty have not been independently extracted or appraised. Read the complete abstract and linked paper before interpreting the findings.
Read complete abstract (146 words)
Preclinical mouse models are extensively used in biomedical research to gain insight into disease mechanisms and to test new drug treatments. Glucose and insulin tolerance tests are simple experimental tests frequently used worldwide to assess glucose metabolism in mice. Various guidelines and methodological considerations have been published to help researchers standardize procedures and optimize research outcomes. Yet, there is still important experimental heterogeneity in the way these simple procedures are performed, with no real consensus on what the best practices are to achieve high-quality research and reproducible results. Here we critically examine several published guidelines and recent technical reports on how to perform these metabolic tests in laboratory mice and discuss the influence of various confounding factors on test results. We hope this work will help scientists establish more consensual guidelines for maximizing the relevance and clinical translation of studies using mouse models in metabolic research.
Abstract reproduced from the open-access article under the recorded cc by licence; © the authors. PMID 39587363 · DOI · PMC11695259
Key reviews and meta-analyses
The most cited review-type papers among PubMed’s best-match results for this heading, listed with bibliographic details only. Follow the links for the full text and access terms.
- Research article Gestational diabetes and pregnancy outcomes--a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria [5]
- Research article A1C versus glucose testing: a comparison [6]
- Research article Insulin secretion capacity in the development from normal glucose tolerance to type 2 diabetes [7]
- Research article Assessment of beta-cell function in humans, simultaneously with insulin sensitivity and hepatic extraction, from intravenous and oral glucose tests [8]
- Research article Glucose intolerance and aging [9]
- Research article NIH consensus development conference: diagnosing gestational diabetes mellitus [10]
References
- Borai A, Livingstone C, Kaddam I, Ferns G. Selection of the appropriate method for the assessment of insulin resistance. . 2011. PMID 22112229 · DOI · PMC3258205
- Wang Q, Jokelainen J, Auvinen J, Puukka K, Keinänen-Kiukaanniemi S, Järvelin MR, et al.. Insulin resistance and systemic metabolic changes in oral glucose tolerance test in 5340 individuals: an interventional study. . 2019. PMID 31779625 · DOI · PMC6883544
- Lu W, Xu Y, Shao X, Gao F, Li Y, Hu J, et al.. Uric Acid Produces an Inflammatory Response through Activation of NF-κB in the Hypothalamus: Implications for the Pathogenesis of Metabolic Disorders. . 2015. PMID 26179594 · DOI · PMC4503982
- Moro C, Magnan C. Revisited guidelines for metabolic tolerance tests in mice. . 2025. PMID 39587363 · DOI · PMC11695259
- Wendland EM, Torloni MR, Falavigna M, Trujillo J, Dode MA, Campos MA, et al.. Gestational diabetes and pregnancy outcomes--a systematic review of the World Health Organization (WHO) and the International Association of Diabetes in Pregnancy Study Groups (IADPSG) diagnostic criteria. BMC Pregnancy Childbirth. 2012. PMID 22462760 · DOI · PMC3352245
- Sacks DB. A1C versus glucose testing: a comparison. Diabetes Care. 2011. PMID 21270207 · DOI · PMC3024379
- Fukushima M, Suzuki H, Seino Y. Insulin secretion capacity in the development from normal glucose tolerance to type 2 diabetes. Diabetes Res Clin Pract. 2004. PMID 15563978 · DOI
- Cobelli C, Toffolo GM, Dalla Man C, Campioni M, Denti P, Caumo A, et al.. Assessment of beta-cell function in humans, simultaneously with insulin sensitivity and hepatic extraction, from intravenous and oral glucose tests. Am J Physiol Endocrinol Metab. 2007. PMID 17341552 · DOI
- DeFronzo RA. Glucose intolerance and aging. Diabetes Care. 1981. PMID 7049632 · DOI
- Vandorsten JP, Dodson WC, Espeland MA, Grobman WA, Guise JM, Mercer BM, et al.. NIH consensus development conference: diagnosing gestational diabetes mellitus. NIH Consens State Sci Statements. 2013. PMID 23748438
About this article
- Sources
- NLM Medical Subject Headings (definition, synonyms); NCBI PubMed (counts, identifiers); Europe PMC (open-access abstracts and citation counts).
- Verification
- Every reference was resolved against PubMed on 2026-11-04; title, year and DOI matched.
- Review state
- automated source verified; no human expert review has been recorded yet.
- Version
- 1, last updated 2026-11-04.
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Evidence strength
EvidenceHelix's computed view of the study designs behind this topic: how common meta-analyses, randomized trials and reviews are, how recent the literature is, and a plain-language reading. It describes the design mix of the literature. It is not a quality appraisal of any study, and it is not medical advice.