ArticleCurrent cardiovascular risk reports2013
The Ups and Downs of Insulin Resistance and Type 2 Diabetes: Lessons from Genomic Analyses in Humans.
Article in Current cardiovascular risk reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Genome Wide Association Study Identifies theJournal of the American Heart Association · 2018Pooled it
- Individuation of a cut-off value of triglyceride-glucose index for incident diabetes mellitus in patients with essential hypertension.Internal and emergency medicine · 2025Article
- Common and ethnic-specific derangements in skeletal muscle transcriptome associated with obesity.International journal of obesity (2005) · 2024Article
- miRDM-rfGA: Genetic algorithm-based identification of a miRNA set for detecting type 2 diabetes.BMC medical genomics · 2023Article
- Multi-omic integration reveals cell-type-specific regulatory networks of insulin resistance in distinct ancestry populations.Cell systems · 2023Article
- Shared Genetic Basis and Causal Relationship Between Television Watching, Breakfast Skipping and Type 2 Diabetes: Evidence From a Comprehensive Genetic Analysis.Frontiers in endocrinology · 2022Article
- Article
- Transcriptional Regulatory Mechanisms in Adipose and Muscle Tissue Associated with Composite Glucometabolic Phenotypes.Obesity (Silver Spring, Md.) · 2018Article
- Tissue-Specific and Genetic Regulation of Insulin Sensitivity-Associated Transcripts in African Americans.The Journal of clinical endocrinology and metabolism · 2016Article
- Obesity Associated Modulation of miRNA and Co-Regulated Target Transcripts in Human Adipose Tissue of Non-Diabetic Subjects.MicroRNA (Shariqah, United Arab Emirates) · 2015Article
- Linking maternal obesity to early insulin resistance.Molecular metabolism · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
We are in the midst of a worldwide epidemic of type 2 diabetes (T2D) and obesity. Understanding the mechanisms underlying these diseases is critical if we are to halt their progression and ultimately prevent their development. The advent and widespread implementation of microarray technology has allowed analysis of small samples of human skeletal muscle, adipose, liver, pancreas and blood. While patterns differ in each tissue, several dominant themes have emerged from these studies, including altered expression of genes indicating increased inflammation and altered lipid and mitochondrial oxidative metabolism and insulin signaling in patients with T2D, and in some cases, in those at risk for disease. Unraveling which changes in gene expression are primary, and which are secondary to an insulin resistant or diabetes metabolic milieu remains a scientific challenge but we are one step closer.
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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.