title: Predictive value of ankle-brachial index and blood glucose on the outcomes of six-year all-cause mortality and cardiovascular mortality in a Chinese population of type 2 diabetes patients.
abstract: OBJECTIVE
  The aim of this paper was to investigate the predictive value of ankle-brachial index (ABI) and blood glucose to estimate mortality in Chinese type 2 diabetes patients.
  
  
  METHODS
  Chinese type 2 diabetes patients (1706 subjects) were followed up for about 6 years,
  
  
  RESULTS
  One thousand four hundred fourteen were included in the final statistical analysis during a median follow-up of 69 months. Overall, 398 patients died during follow-up and 254 deaths were attributable to cardiovascular disease (CVD). Relative risks of all-cause and CVD mortality of patients with ABI≤0.4 were increased by 2.073-fold (95% CI: 1.236-3.478) and 3.086-fold (95% CI: 1.702-5.595), compared with those of patients with ABI>1.0 and ≤1.4. Mortality was significantly increased with increasing plasma glucose and decreasing ABI. All-cause and CVD mortality was the highest (55.6% and 44.4%) with ABI≤0.4 and plasma glucose >10 mmol/L simultaneously. Relative risks of all-cause and CVD mortality in these patients were increased by 3.905-fold (95% CI: 1.334-11.431) and 3.771-fold (95% CI: 1.079-13.171), compared with patients with ABI>1.0 and ≤1.4, and plasma glucose ≤6 mmol/L simultaneously. Models to evaluate additional predictive value of ABI for mortality of type 2 diabetes patients suggested that addition of ABI significantly improved the prediction of the death rate compared with the model including conventional risk factors only.
  
  
  CONCLUSIONS
  High plasma glucose and low ABI had synergistic effects on increasing mortality of type 2 diabetes patients. The addition of ABI can significantly improve the prediction of mortality compared to protocol using conventional risk factors only.
authors.affiliation: Department of Cardiology, Shanghai Tenth People's Hospital, Tongji University School of Medicine, China.
authors.forename: Y
authors.initials: Y
authors.lastname: Wang
authors.forename: Q
authors.initials: Q
authors.lastname: Mou
authors.forename: D
authors.initials: D
authors.lastname: Zhao
authors.forename: Y
authors.initials: Y
authors.lastname: Xu
authors.forename: D
authors.initials: D
authors.lastname: Hu
authors.forename: H
authors.initials: H
authors.lastname: Ma
authors.forename: J
authors.initials: J
authors.lastname: Liu
authors.forename: X
authors.initials: X
authors.lastname: Guo
authors.forename: J
authors.initials: J
authors.lastname: Li
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D055109:Ankle Brachial Index; D044466:Asian People; D015415:Biomarkers / Q000097:blood; D001786:Blood Glucose / Q000032:analysis*; D002318:Cardiovascular Diseases / Q000097:blood / Q000175:diagnosis* / Q000208:ethnology / Q000401:mortality*; D002423:Cause of Death; D016009:Chi-Square Distribution; D002681:China / Q000453:epidemiology; D003924:Diabetes Mellitus, Type 2 / Q000097:blood / Q000175:diagnosis* / Q000208:ethnology / Q000401:mortality*; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D011237:Predictive Value of Tests; D016016:Proportional Hazards Models; D011446:Prospective Studies; D018570:Risk Assessment; D012307:Risk Factors; D013997:Time Factors
chemical_list: D015415:Biomarkers; D001786:Blood Glucose