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Nutr Metab Cardiovasc Dis ; 23(4): 300-6, 2013 Apr.
Article in English | MEDLINE | ID: mdl-22633797

ABSTRACT

BACKGROUND AND AIM: The aim of the present case-control study is to explore the effect of case mix on the relationship between glycated haemoglobin (HbA1c) and mortality in type 2 diabetic patients. METHODS AND RESULTS: A nested case-control study data set was generated from the cohort-study data set (n = 4140 type 2 diabetic outpatients) by sampling controls from the risk sets. Cases (n = 427) were compared with an equal number of controls chosen from those members of the cohort who were at risk for the same follow-up time of the case, matched for age (±3 years), sex, body mass index (BMI) (±2 kg m(-2)), duration of diabetes (±5 years), and Charlson's Comorbidity Score (CCS) (±1). The main predefined analysis was the comparison of cases and controls for proportion of patients with each HbA1c class (<6.5%, 6.5-7.4%, 7.5-8.4% and ≥8.5%). During a mean follow-up of 5.7 ± 3.5 years, 427 deaths were recorded. The lowest risk of death was observed in the HbA1c 6.5-7.4% category; a lower HbA1c was associated with a non-significant trend towards a higher risk. The risk associated with a low (<6.5%) HbA1c was significantly greater in patients who were insulin-treated than in the rest of the sample. CONCLUSIONS: The present study suggests that glycaemic targets should be individualised on the basis of the characteristics of each patient, considering age, co-morbidity and duration of diabetes. Caution should be used in prescribing insulin to reach near-normoglycaemia, particularly in older, frail patients.


Subject(s)
Diabetes Mellitus, Type 2/blood , Diabetes Mellitus, Type 2/mortality , Glycated Hemoglobin/analysis , Precision Medicine , Age Factors , Aged , Aged, 80 and over , Biomarkers/blood , Case-Control Studies , Chi-Square Distribution , Comorbidity , Diabetes Mellitus, Type 2/drug therapy , Female , Frail Elderly , Humans , Hypoglycemic Agents/adverse effects , Insulin/adverse effects , Italy/epidemiology , Logistic Models , Male , Middle Aged , Prognosis , Risk Assessment , Risk Factors , Time Factors
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