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Factors Enhancing Serum Syndecan-1 Concentrations: A Large-Scale Comprehensive Medical Examination.

Kazumasa OdaHideshi OkadaAkio SuzukiHiroyuki TomitaRyo KobayashiKazuyuki SumiKodai SuzukiChihiro TakadaTakuma IshiharaKeiko SuzukiSoichiro KanoKohei KondoYuki IwashitaHirohisa YanoRyogen ZaikokujiSo SampeiTetsuya FukutaYuichiro KitagawaHaruka OkamotoTakatomo WatanabeTomonori KawaguchiTakao KojimaFumiko DeguchiNagisa MiyazakiNoriaki YamadaTomoaki DoiTakahiro YoshidaHiroaki UshikoshiShozo YoshidaGenzou TakemuraShinji Ogura
Published in: Journal of clinical medicine (2019)
Endothelial disorders are related to various diseases. An initial endothelial injury is characterized by endothelial glycocalyx injury. We aimed to evaluate endothelial glycocalyx injury by measuring serum syndecan-1 concentrations in patients during comprehensive medical examinations. A single-center, prospective, observational study was conducted at Asahi University Hospital. The participants enrolled in this study were 1313 patients who underwent comprehensive medical examinations at Asahi University Hospital from January 2018 to June 2018. One patient undergoing hemodialysis was excluded from the study. At enrollment, blood samples were obtained, and study personnel collected demographic and clinical data. No treatments or exposures were conducted except for standard medical examinations and blood sample collection. Laboratory data were obtained by the collection of blood samples at the time of study enrolment. According to nonlinear regression, the concentrations of serum syndecan-1 were significantly related to age (p = 0.016), aspartic aminotransferase concentration (AST, p = 0.020), blood urea nitrogen concentration (BUN, p = 0.013), triglyceride concentration (p < 0.001), and hematocrit (p = 0.006). These relationships were independent associations. Endothelial glycocalyx injury, which is reflected by serum syndecan-1 concentrations, is related to age, hematocrit, AST concentration, BUN concentration, and triglyceride concentration.
Keyphrases
  • healthcare
  • newly diagnosed
  • ejection fraction
  • electronic health record
  • machine learning
  • artificial intelligence
  • affordable care act