ArticleBMC proceedings2016
Analysis of gene expression to predict dynamics of future hypertension incidence in type 2 diabetic patients.
Article in BMC proceedings, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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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.
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Who cites it
5 citing papers in PubMed, 10 citations in OpenAlex.
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- Single-nucleotide polymorphisms in a cohort of significantly obese women without cardiometabolic diseases.International journal of obesity (2005) · 2019Article
- Regulation of an Opioid Receptor Chaperone Protein, RTP4, by Morphine.Molecular pharmacology · 2019Article
- Gene expression in large pedigrees: analytic approaches.BMC genetics · 2016Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
backgroundThe main focus of the Genetic Analysis Workshop 19 (GAW19) is identification of genes related to the occurrence of hypertension in the cohort of patients with type 2 diabetes mellitus (T2DM). The aim of our study was to predict dynamics of the future hypertension incidence, based on gene expression profiles, systolic and diastolic blood pressure changes in time, sex, baseline age, and cigarette smoking status. We analyzed data made available to GAW19 participants, which included gene expression profiles of peripheral blood mononuclear cells (PBMCs) from the diabetic members of 20 Mexican American families.
methodsOn the basis of mid blood pressure measurements at several time points, the coefficient of regression (slope) was calculated for each individual. We corrected the slope value in patients treated with antihypertensive medications. Feature preprocessing methods were used to remove highly correlated probes and linear dependencies between them. Subsequently, multiple linear regression model was used to associate gene expression with the regression coefficient calculated for each T2DM patient. Tenfold cross-validation was used to validate the model. We used linear mixed effects model and kinship coefficients to account for the family structure. All calculations were performed in R.
resultsThis analysis allowed us to identify 6 well-annotated genes:
conclusionsThere is no obvious mechanism that links all detected genes with dynamics of hypertension incidence. Identification of possible connection with hypertension needs further investigation.
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.