title: Profiles of lacunar and nonlacunar stroke.
abstract: OBJECTIVE
  Determining which small deep infarcts (SDIs) are of lacunar, arterial, or cardioembolic etiology is challenging, but important in delivering optimal stroke prevention therapy. We sought to distinguish lacunar from nonlacunar causes of SDIs using a gene expression profile.
  
  
  METHODS
  A total of 184 ischemic strokes were analyzed. Lacunar stroke was defined as a lacunar syndrome with infarction <15mm in a region supplied by penetrating arteries. RNA from blood was processed on whole genome microarrays. Genes differentially expressed between lacunar (n = 30) and nonlacunar strokes (n = 86) were identified (false discovery rate ≤ 0.05, fold change >|1.5|) and used to develop a prediction model. The model was evaluated by cross-validation and in a second test cohort (n = 36). The etiology of SDIs of unclear cause (SDIs ≥ 15mm or SDIs with potential embolic source) (n = 32) was predicted using the derived model.
  
  
  RESULTS
  A 41-gene profile discriminated lacunar from nonlacunar stroke with >90% sensitivity and specificity. Of the 32 SDIs of unclear cause, 15 were predicted to be lacunar, and 17 were predicted to be nonlacunar. The identified profile represents differences in immune response between lacunar and nonlacunar stroke.
  
  
  CONCLUSIONS
  Profiles of differentially expressed genes can distinguish lacunar from nonlacunar stroke. SDIs of unclear cause were frequently predicted to be of nonlacunar etiology, suggesting that comprehensive workup of SDIs is important to identify potential cardioembolic and arterial causes. Further study is required to evaluate the gene profile in an independent cohort and determine the clinical and treatment implications of SDIs of predicted nonlacunar etiology.
authors.affiliation: Department of Neurology and the MIND Institute, University of California at Davis, Sacramento, CA 95817, USA. gcjickling@ucdavis.edu
authors.forename: Glen C
authors.initials: GC
authors.lastname: Jickling
authors.forename: Boryana
authors.initials: B
authors.lastname: Stamova
authors.forename: Bradley P
authors.initials: BP
authors.lastname: Ander
authors.forename: Xinhua
authors.initials: X
authors.lastname: Zhan
authors.forename: Yingfang
authors.initials: Y
authors.lastname: Tian
authors.forename: Dazhi
authors.initials: D
authors.lastname: Liu
authors.forename: Huichun
authors.initials: H
authors.lastname: Xu
authors.forename: S Claiborne
authors.initials: SC
authors.lastname: Johnston
authors.forename: Piero
authors.initials: P
authors.lastname: Verro
authors.forename: Frank R
authors.initials: FR
authors.lastname: Sharp
mesh_terms: D000368:Aged; D001921:Brain / Q000473:pathology*; D002536:Cerebral Arteries / Q000473:pathology; D002544:Cerebral Infarction / Q000175:diagnosis / Q000235:genetics / Q000473:pathology*; D015331:Cohort Studies; D003627:Data Interpretation, Statistical; D003937:Diagnosis, Differential; D005260:Female; D015870:Gene Expression / Q000502:physiology; D020869:Gene Expression Profiling; D006801:Humans; D007249:Inflammation / Q000473:pathology; D020766:Intracranial Embolism / Q000150:complications / Q000235:genetics / Q000473:pathology; D008297:Male; D046228:Microarray Analysis; D008875:Middle Aged; D015203:Reproducibility of Results; D012307:Risk Factors; D020521:Stroke / Q000175:diagnosis / Q000209:etiology / Q000235:genetics*; D014057:Tomography, X-Ray Computed