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Risk Factors for the Progression or Regression to Diabetes or Normoglycaemia for Men with Impaired Fasting Glucose
Jacob W Harland1, Natalie K Hyde1, Zoe Shih-Jung Liu1
1Deakin University, IMPACT-Institute for Mental and Physical Health and Clinical Translation, Geelong, Australia.
Aims:
Reporting of impaired fasting glucose (IFG) prevalence and risk factors for progression to diabetes differs between studies, in part, due to the use of multiple definitions of the condition. We aimed to determine the prevalence of diabetes and IFG in men and identify risk factors for the progression to diabetes and regression to normoglycaemia.
Materials And Methods:
Participants were from the Geelong Osteoporosis Study. Diabetes was defined as fasting plasma glucose (FPG) ≥ 7.0 mmol/L, self-report of diabetes and/or use of antihyperglycaemic medication. IFG was defined using both American Diabetes Association (IFG-ADA: FPG 5.6-6.9 mmol/L) and World Health Organisation (IFG-WHO: FPG 6.1-6.9 mmol/L) criteria. Prevalence of hyperglycaemia at baseline (2001-2006, 1170 men, 20-97 years) was age-standardised to the 2006 Australian population. Multivariable logistic regression models (n = 416) were used to identify risk factors for the progression to diabetes and regression to normoglycaemia over 15 years.
Results:
Age-standardised prevalence of IFG-ADA was 18.2% (95% CI 15.7-20.7), 6.2% for IFG-WHO (95% CI 4.8-7.6) and 7.3% for diabetes (95% CI 5.8-8.8). Higher FPG, glycated haemoglobin, age, body fat percent and lower HDL cholesterol were associated with progression to diabetes, whereas younger age and higher HDL cholesterol were associated with regression. A 1.0 mmol/L increase in FPG resulted in a sixfold greater chance of progression to diabetes over follow-up (OR 6.44, 95% CI 2.97-13.94; p < 0.001). A prediction model containing age, FPG, HDL cholesterol and HbA1c predicted an optimal FPG cut point for progression of 5.3 mmol/L.
Conclusion:
This study reports cut points for predicting progression to diabetes that align with the ADA classification of IFG. These data may support future work investigating diabetes prevention strategies.
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