title: New aspects of the insulin resistance syndrome: impact on haematological parameters.
abstract: OBJECTIVE
  Previous studies have shown that insulin has an important in vitro role in the regulation of human erythropoiesis. We investigated whether in vivo hyperinsulinaemia/insulin resistance affects haematological parameters.
  
  
  METHODS
  A total of 608 subjects between 22 and 99 years of age were enrolled in the Chianti study, an epidemiological study of factors affecting mobility in old age. The degree of insulin resistance was assessed using the homeostasis model.
  
  
  RESULTS
  We found a correlation between insulin resistance and red blood cell count, (r = 0.14 p < 0.001), plasma haemoglobin (r = 0.16 p < 0.001), haematocrit (r = 0.15 p < 0.001) and plasma iron (r = 0.1 p < 0.05) concentrations. Red blood cell count was also associated with the other biological markers of insulin resistance syndrome. Subjects with higher insulin resistance (4 degrees quartile) had higher red blood cell count, plasma triglycerides and low density lipoproteins (LDL) cholesterol concentrations and lower high density lipoproteins (HDL) cholesterol concentrations then subjects at the lowest quartiles of insulin resistance. Insulin resistance and BMI were significant and independent predictors of red blood cell count even when the analysis was adjusted for age, sex, waist-to-hip ratio, plasma iron and drug intake.
  
  
  CONCLUSIONS
  Our findings provide in vivo evidence of a relation between hyperinsulinaemia/insulin resistance, the main variables of insulin resistance syndrome and erythropoiesis. Increased red blood cell count could be considered as a new aspect of the insulin resistance syndrome that could contribute to the increased risk of developing cardiovascular problems.
authors.affiliation: Department of Geriatric Medicine and Metabolic Diseases, University of Naples, Italy.
authors.forename: M
authors.initials: M
authors.lastname: Barbieri
authors.forename: E
authors.initials: E
authors.lastname: Ragno
authors.forename: E
authors.initials: E
authors.lastname: Benvenuti
authors.forename: G A
authors.initials: GA
authors.lastname: Zito
authors.forename: A
authors.initials: A
authors.lastname: Corsi
authors.forename: L
authors.initials: L
authors.lastname: Ferrucci
authors.forename: G
authors.initials: G
authors.lastname: Paolisso
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D015992:Body Mass Index; D008076:Cholesterol, HDL / Q000097:blood; D008078:Cholesterol, LDL / Q000097:blood; D004906:Erythrocyte Count; D006400:Hematocrit; D006454:Hemoglobins / Q000032:analysis; D006801:Humans; D006946:Hyperinsulinism / Q000097:blood*; D007333:Insulin Resistance; D007501:Iron / Q000097:blood; D007558:Italy; D016014:Linear Models; D008875:Middle Aged; D011446:Prospective Studies; D012907:Smoking; D014280:Triglycerides / Q000097:blood
chemical_list: D008076:Cholesterol, HDL; D008078:Cholesterol, LDL; D006454:Hemoglobins; D014280:Triglycerides; D007501:Iron