Stay Connected. Manage Your Care.
Access your health information anytime and anywhere, at home or on the go, with MyHealth.
- Message your clinic
- View your lab results
- Schedule your next appointment
- Pay your bill
The MyHealth mobile app from Stanford Health Care puts all your health information at your fingertips and makes managing your health care simple and quick.
Guest Services
24/7
We are available to assist you
whenever you need it. Give us a call at
650-498-3333 or
PHYSICIAN HELPLINE
Have a question? We're here to help! Call 1-866-742-4811
Monday - Friday, 8 a.m. - 5 p.m.
REFER A PATIENT
Fax 650-320-9443
Track your patients' progress and communicate with Stanford providers conveniently and securely.
Abstract
The propensity for developing atherosclerosis is dependent on underlying genetic risk and varies as a function of age and exposure to environmental risk factors. Employing three mouse models with different disease susceptibility, two diets, and a longitudinal experimental design, it was possible to manipulate each of these factors to focus analysis on genes most likely to have a specific disease-related function. To identify differences in longitudinal gene expression patterns of atherosclerosis, we have developed and employed a statistical algorithm that relies on generalized regression and permutation analysis. Comprehensive annotation of the array with ontology and pathway terms has allowed rigorous identification of molecular and biological processes that underlie disease pathophysiology. The repertoire of atherosclerosis-related immunomodulatory genes has been extended, and additional fundamental pathways have been identified. This highly disease-specific group of mouse genes was combined with an extensive human coronary artery data set to identify a shared group of genes differentially regulated among atherosclerotic tissues from different species and different vascular beds. A small core subset of these differentially regulated genes was sufficient to accurately classify various stages of the disease in mouse. The same gene subset was also found to accurately classify human coronary lesion severity. In addition, this classifier gene set was able to distinguish with high accuracy atherectomy specimens from native coronary artery disease vs. those collected from in-stent restenosis lesions, thus identifying molecular differences between these two processes. These studies significantly focus efforts aimed at identifying central gene regulatory pathways that mediate atherosclerotic disease, and the identification of classification gene sets offers unique insights into potential diagnostic and therapeutic strategies in atherosclerotic disease.
View details for DOI 10.1152/physiolgenomics.00001.2005
View details for Web of Science ID 000230987900011
View details for PubMedID 15870398