ArticleNature medicine2022
Proteomic signatures for identification of impaired glucose tolerance.
Article in Nature medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled 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.
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.
Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it, 42 citations in OpenAlex.
- Pooled it
- Growth Hormone Mediators and Glycemic Control in Youths With Type 2 Diabetes: A Secondary Analysis of a Randomized Clinical Trial.JAMA network open · 2024Trial
- Large-Scale Proteomics Uncovers Pre-Disease Inflammation-Lipid Subtypes to Refine Risk Stratification and Prediction of Type 2 Diabetes.Diabetes, obesity & metabolism · 2026Article
- Proteomic Signatures of 3-Year Progression From Impaired Fasting Glucose to Diabetes: The Atherosclerosis Risk in Communities (ARIC) Study.Diabetes care · 2026Article
- Genetic and molecular signatures highlight diverse pathways linking obesity to type 2 diabetes.Nature communications · 2026Article
- Proteomic health archetypes identified in disease-free adults enable risk assessment for diverse chronic diseases.Genome medicine · 2026Article
- Data-driven prioritization of high-risk individuals for weight loss interventions.Nature medicine · 2026Article
- Large-scale multi-omics enhance risk prediction for type 2 diabetes.Cardiovascular diabetology · 2026Article
- Proteomic signature of dementia risk in type 2 diabetes.Journal of advanced research · 2026Article
- Plasma Proteomic Signatures of Glucose Metabolism Disturbances and Early Diabetes.International journal of molecular sciences · 2026Article
- Muscle and Liver Insulin Resistance Are Associated with Distinct Plasma Protein Profiles: Data from the DiOGenes Trial.The Journal of clinical endocrinology and metabolism · 2026Article
- Beyond Glucose-Rethinking Prediabetes for Precision Prevention.Diabetes care · 2026Review
- Navigating ambivalence: perspectives on healthy eating habits and readiness for change among Swedish men with prediabetes.Frontiers in clinical diabetes and healthcare · 2026Article
- Sex-specific proteomic signatures improve cardiovascular risk prediction for the general population without cardiovascular disease or diabetes.Journal of advanced research · 2026Article
- Artificial intelligence in prediabetes care: applications in screening, risk prediction, and lifestyle intervention.Frontiers in endocrinology · 2026Review
- Proteome atlas for mechanistic discovery and risk prediction of diabetic retinopathy.Nature communications · 2025Article
- Circulating inflammation-related proteome improves cardiovascular risk prediction. Results from two large European cohort studies.European journal of epidemiology · 2025Article
- Multimodal AI correlates of glucose spikes in people with normal glucose regulation, pre-diabetes and type 2 diabetes.Nature medicine · 2025Article
- The coming era of proteomics-driven precision medicine.National science review · 2025Review
- Large-scale evaluation of proteomic and polygenic risk scores reveals complementary contributions to incident disease prediction.medRxiv : the preprint server for health sciences · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors at 5 institutions in 4 countries.
Funding
Abstract
The implementation of recommendations for type 2 diabetes (T2D) screening and diagnosis focuses on the measurement of glycated hemoglobin (HbA1c) and fasting glucose. This approach leaves a large number of individuals with isolated impaired glucose tolerance (iIGT), who are only detectable through oral glucose tolerance tests (OGTTs), at risk of diabetes and its severe complications. We applied machine learning to the proteomic profiles of a single fasted sample from 11,546 participants of the Fenland study to test discrimination of iIGT defined using the gold-standard OGTTs. We observed significantly improved discriminative performance by adding only three proteins (RTN4R, CBPM and GHR) to the best clinical model (AUROC = 0.80 (95% confidence interval: 0.79-0.86), P = 0.004), which we validated in an external cohort. Increased plasma levels of these candidate proteins were associated with an increased risk for future T2D in an independent cohort and were also increased in individuals genetically susceptible to impaired glucose homeostasis and T2D. Assessment of a limited number of proteins can identify individuals likely to be missed by current diagnostic strategies and at high risk of T2D and its complications.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.