SynthesisScientific reports2013
A computational approach to chemical etiologies of diabetes.
Synthesis in Scientific reports, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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
17 citing papers in PubMed, 33 citations in OpenAlex.
- Plasma Concentrations of Per- and Polyfluoroalkyl Substances at Baseline and Associations with Glycemic Indicators and Diabetes Incidence among High-Risk Adults in the Diabetes Prevention Program Trial.Environmental health perspectives · 2017Trial
- Diabetogenic effects of arsenic exposure in C57BL/6 N mice carrying the human AS3MT gene: the role of sex and exposure window.Archives of toxicology · 2026Article
- Gene-environment interactions in the associations of PFAS exposure with insulin sensitivity and beta-cell function in a Faroese cohort followed from birth to adulthood.Environmental research · 2023Article
- Obesity III: Obesogen assays: Limitations, strengths, and new directions.Biochemical pharmacology · 2022Review
- Article
- Arsenic Exposure Decreases Adiposity During High-Fat Feeding.Obesity (Silver Spring, Md.) · 2020Article
- Integrative Strategy of Testing Systems for Identification of Endocrine Disruptors Inducing Metabolic Disorders-An Introduction to the OBERON Project.International journal of molecular sciences · 2020Review
- IRS1- rs10498210 G/A and CCR5-59029 A/G polymorphisms in patients with type 2 diabetes in Kurdistan.Molecular genetics & genomic medicine · 2019Article
- Linking Bisphenol S to Adverse Outcome Pathways Using a Combined Text Mining and Systems Biology Approach.Environmental health perspectives · 2019Article
- Genetic variants linked to T2DM risk in Kurdish populations.Diabetes, metabolic syndrome and obesity : targets and therapy · 2019Article
- A systems biology approach to predictive developmental neurotoxicity of a larvicide used in the prevention of Zika virus transmission.Toxicology and applied pharmacology · 2018Article
- Current Challenges and New Opportunities for Gene-Environment Interaction Studies of Complex Diseases.American journal of epidemiology · 2017Article
- Early-Life Exposure to Perfluoroalkyl Substances and Childhood Metabolic Function.Environmental health perspectives · 2017Article
- The Next Generation of Risk Assessment Multi-Year Study-Highlights of Findings, Applications to Risk Assessment, and Future Directions.Environmental health perspectives · 2016Review
- Assessment of Toxicological Perturbations and Variants of Pancreatic Islet Development in the Zebrafish Model.Toxics · 2016Article
- Recent advances in the molecular genetics of type 2 diabetes mellitus.World journal of diabetes · 2014Review
- A CTD-Pfizer collaboration: manual curation of 88,000 scientific articles text mined for drug-disease and drug-phenotype interactions.Database : the journal of biological databases and curation · 2013Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Computational meta-analysis can link environmental chemicals to genes and proteins involved in human diseases, thereby elucidating possible etiologies and pathogeneses of non-communicable diseases. We used an integrated computational systems biology approach to examine possible pathogenetic linkages in type 2 diabetes (T2D) through genome-wide associations, disease similarities, and published empirical evidence. Ten environmental chemicals were found to be potentially linked to T2D, the highest scores were observed for arsenic, 2,3,7,8-tetrachlorodibenzo-p-dioxin, hexachlorobenzene, and perfluorooctanoic acid. For these substances we integrated disease and pathway annotations on top of protein interactions to reveal possible pathogenetic pathways that deserve empirical testing. The approach is general and can address other public health concerns in addition to identifying diabetogenic chemicals, and offers thus promising guidance for future research in regard to the etiology and pathogenesis of complex diseases.
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.