Insulin resistance is associated with nonalcoholic fatty liver disease (NAFLD) and is a major factor in the pathogenesis of type 2 diabetes. The development of hepatic insulin resistance has been ascribed to multiple causes, including inflammation, endoplasmic reticulum (ER) stress, and accumulation of hepatocellular lipids in animal models of NAFLD. However, it is unknown whether these same cellular mechanisms link insulin resistance to hepatic steatosis in humans. To examine the cellular mechanisms that link hepatic steatosis to insulin resistance, we comprehensively assessed each of these pathways by using flash-frozen liver biopsies obtained from 37 obese, nondiabetic individuals and correlating key hepatic and plasma markers of inflammation, ER stress, and lipids with the homeostatic model assessment of insulin resistance index. We found that hepatic diacylglycerol (DAG) content in cytoplasmic lipid droplets was the best predictor of insulin resistance (R = 0.80, P < 0.001), and it was responsible for 64% of the variability in insulin sensitivity. Hepatic DAG content was also strongly correlated with activation of hepatic PKCe (R = 0.67, P < 0.001), which impairs insulin signaling. In contrast, there was no significant association between insulin resistance and other putative lipid metabolites or plasma or hepatic markers of inflammation. ER stress markers were only partly correlated with insulin resistance. In conclusion, these data show that hepatic DAG content in lipid droplets is the best predictor of insulin resistance in humans, and they support the hypothesis that NAFLD-associated hepatic insulin resistance is caused by an increase in hepatic DAG content, which results in activation of PKCe.

https://www.pnas.org/content/pnas/108/39/16381.full.pdf

Cellular mechanism of insulin resistance in nonalcoholic fatty liver disease

Large-scale DNA databanks linked to electronic medical record (EMR) systems have been proposed as an approach for rapidly generating large, diverse cohorts for discovery and replication of genotype-phenotype associations. However, the extent to which such resources are capable of delivering on this promise is unknown. We studied whether an EMR-linked DNA biorepository can be used to detect known genotype-phenotype associations for five diseases. Twenty-one SNPs previously implicated as common variants predisposing to atrial fibrillation, Crohn disease, multiple sclerosis, rheumatoid arthritis, or type 2 diabetes were successfully genotyped in 9483 samples accrued over 4 mo into BioVU, the Vanderbilt University Medical Center DNA biobank. Previously reported odds ratios (ORPR) ranged from 1.14 to 2.36. For each phenotype, natural language processing techniques and billing-code queries were used to identify cases (n = 70–698) and controls (n = 808–3818) from deidentified health records. Each of the 21 tests of association yielded point estimates in the expected direction. Previous genotype-phenotype associations were replicated (p 1.25, and in 0/7 with lower ORPR. Statistically significant associations were detected in all analyses that were adequately powered. In each of the five diseases studied, at least one previously reported association was replicated. These data demonstrate that phenotypes representing clinical diagnoses can be extracted from EMR systems, and they support the use of DNA resources coupled to EMR systems as tools for rapid generation of large data sets required for replication of associations found in research cohorts and for discovery in genome science.

Robust Replication of Genotype-Phenotype Associations across Multiple Diseases in an Electronic Medical Record

The convergence of two rapidly developing technologies - high-throughput genotyping and electronic health records (EHRs) - gives scientists an unprecedented opportunity to utilize routine healthcare data to accelerate genomic discovery. Institutions and healthcare systems have been building EHR-linked DNA biobanks to enable such a vision. However, the precise extraction of detailed disease and drug-response phenotype information hidden in EHRs is not an easy task. EHR-based studies have successfully replicated known associations, made new discoveries for diseases and drug response traits, rapidly contributed cases and controls to large meta-analyses, and demonstrated the potential of EHRs for broad-based phenome-wide association studies. In this review, we summarize the advantages and challenges of repurposing EHR data for genetic research. We also highlight recent notable studies and novel approaches to provide an overview of advanced EHR-based phenotyping.

/pdf/extracting-research-quality-phenotypes-from-electronic-3vor2cw0tv.pdf

Extracting research-quality phenotypes from electronic health records to support precision medicine.

Context Associations between chromosome 9p21 single-nucleotide polymorphisms (SNPs) and heart disease have been reported and replicated. If testing improves risk assessments using traditional factors, it may provide opportunities to improve public health. Objectives To perform a targeted systematic review of published literature for effect size, heterogeneity, publication bias, and strength of evidence and to consider whether testing might provide clinical utility. Data Sources Electronic search via HuGE Navigator through January 2009 and review of reference lists from included articles. Study Selection English-language articles that tested for 9p21 SNPs with coronary heart/artery disease or myocardial infarction as primary outcomes. Included articles also provided race, numbers of participants, and data to compute an odds ratio (OR). Articles were excluded if reporting only intermediate outcomes (eg, atherosclerosis) or if all participants had existing disease. Twenty-five articles were initially identified and 16 were included. A follow-up search identified 6 additional articles. Data Extraction Independent extraction was performed by 2 reviewers and consensus was reached. Credibility of evidence was assessed using published Venice criteria. Data Synthesis Forty-seven distinct data sets from the 22 articles were analyzed, including 35 872 cases and 95 837 controls. The summary OR for heart disease among individuals with 2 vs 1 at-risk alleles was 1.25 (95% confidence interval [CI], 1.21-1.29), with low to moderate heterogeneity. Age at disease diagnosis was a significant covariate, with ORs of 1.35 (95% CI, 1.30-1.40) for age 55 years or younger and 1.21 (95% CI, 1.16-1.25) for age 75 years or younger. For a 65-year-old man, the 10-year heart disease risk for 2 vs 1 at-risk alleles would be 13.2% vs 11%. For a 40-year-old woman, the 10-year heart disease risk for 2 vs 1 at-risk alleles would be 2.4% vs 2.0%. Nearly identical but inverse results were found when comparing 1 vs 0 at-risk alleles. Three studies showed net reclassification indexes ranging from −0.1% to 4.8%. Conclusion We found a statistically significant association between 9p21 SNPs and heart disease that varied by age at disease onset, but the magnitude of the association was small.

https://jamanetwork.com/journals/jama/articlepdf/185371/jcr05001_648_656.pdf

Association Between 9p21 Genomic Markers and Heart Disease: A Meta-analysis

To investigate the genetic architecture of severe obesity, the authors performed a genome-wide association study of 775 cases and 3197 unascertained controls at ~550 000 markers across the autosomal genome. They found convincing association to the previously described locus including the FTO gene. They also found evidence of association at a further six of 12 other loci previously reported to influence body mass index (BMI) in the general population and one of three associations to severe childhood and adult obesity and that cases have a higher proportion of risk-conferring alleles than controls. The authors found no evidence of homozygosity at any locus due to identity-by-descent associating with phenotype which would be indicative of rare, penetrant alleles, nor was there excess genome-wide homozygosity in cases relative to controls. The results suggest that variants influencing BMI also contribute to severe obesity, a condition at the extreme of the phenotypic spectrum rather than a distinct condition.

/pdf/common-body-mass-index-associated-variants-confer-risk-of-29i9fomfp0.pdf

Common body mass index-associated variants confer risk of extreme obesity

Objective To determine whether 2 single nucleotide polymorphisms (SNPs) in the obesity genes the fat mass and obesity associated gene (FTO) and the insulin induced gene 2 (INSIG2) are associated with class III, or morbid, obesity in patients undergoing bariatric weight loss operations. Design Retrospective analysis of genotype and clinical data. Setting Large rural tertiary care health system. Patients A total of 707 adult patients with a body mass index (BMI; calculated as weight in kilograms divided by height in meters squared) of at least 40 undergoing open or laparoscopic Roux-en-Y gastric bypass operations for morbid obesity or its comorbid medical problems at Geisinger Medical Center, Danville, Pennsylvania. Results The mean BMI in the predominantly white female cohort was 51.2. Approximately 21% of patients were homozygous for theFTO obesity SNP variant, 13% were homozygous for theINSIG2obesity SNP variant, and 3.4% were homozygous for both. Mean BMIs in the groups homozygous for each of these genes were not significantly different from nonhomozygotes. However,FTO/INSIG2double homozygotes and homozygote/heterozygote pairs had significantly higher BMIs than the other groups. Conclusion Increased BMI in morbid obesity is associated with a combination ofFTOandINSIG2SNPs.

https://jamanetwork.com/journals/jamasurgery/articlepdf/401606/soa70071_235_240.pdf

Association of morbid obesity with FTO and INSIG2 allelic variants.

Genomic medicine research requires substantial time and resources to obtain phenotype data. The electronic health record offers potential efficiencies in addressing these temporal and economic challenges, but few studies have explored the feasibility of using such data for genetics research. The main objective of this study was to determine the association of two genetic variants located on chromosome 9p21 conferring susceptibility to coronary heart disease and type 2 diabetes with a variety of clinical phenotypes derived from the electronic health record in a population of morbidly obese patients. Data on more than 100 clinical measures including diagnoses, laboratory values, and medications were extracted from the electronic health records of a total of 709 morbidly obese (body mass index (BMI) ≥ 40 kg/m2) patients. Two common single nucleotide polymorphisms located at chromosome 9p21 recently linked to coronary heart disease and type 2 diabetes (McPherson et al. Science 316:1488–1491, 2007; Saxena et al. Science 316:1331–1336, 2007; Scott et al. Science 316:1341-1345, 2007) were genotyped to assess statistical association with clinical phenotypes. Neither the type 2 diabetes variant nor the coronary heart disease variant was related to any expected clinical phenotype, although high-risk type 2 diabetes/coronary heart disease compound genotypes were associated with several coronary heart disease phenotypes. Electronic health records may be efficient sources of data for validation studies of genetic associations.

/pdf/association-of-chromosome-9p21-snps-with-cardiovascular-uhp7lnsgim.pdf

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data.

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Wanda Krum

Author Tools

Chat about Author

Papers

Association of morbid obesity with FTO and INSIG2 allelic variants.

Association of chromosome 9p21 SNPs with cardiovascular phenotypes in morbid obesity using electronic health record data.