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Articles by B. Balkau
Total Records ( 3 ) for B. Balkau
  L. Chen , D. J. Magliano , B. Balkau , R. Wolfe , L. Brown , A. M. Tonkin , P. Z. Zimmet and J. E. Shaw
  Aims  To evaluate how to most efficiently screen populations to detect people at high risk of incident Type 2 diabetes and those with prevalent, but undiagnosed, Type 2 diabetes.

Methods  Data from 5814 adults in the Australian Diabetes, Obesity and Lifestyle study were used to examine four different types of screening strategies. The strategies incorporated various combinations of cut-points of fasting plasma glucose, the non-invasive Australian Type 2 Diabetes Risk Assessment Tool (AUSDRISK1) and a modified version of the tool incorporating fasting plasma glucose (AUSDRISK2). Sensitivity, specificity, positive predictive value, screening costs per case of incident or prevalent undiagnosed diabetes identified and intervention costs per case of diabetes prevented or reverted were compared.

Results  Of the four strategies that maximized sensitivity and specificity, use of the non-invasive AUSDRISK1, followed by AUSDRISK2 in those found to be at increased risk on AUSDRISK1, had the highest sensitivity (80.3%; 95% confidence interval 76.6-84.1%), specificity (78.1%; 95% confidence interval 76.9-79.2%) and positive predictive value (22.3%; 95% confidence interval 20.2-24.4%) for identifying people with either prevalent undiagnosed diabetes or future incident diabetes. It required the fewest people (24.1%; 95% confidence interval 23.0-25.2%) to enter lifestyle modification programmes, and also had the lowest intervention costs and combined costs of running screening and intervention programmes per case of diabetes prevented or reverted.

Conclusions  Using a self-assessed diabetes risk score as an initial screening step, followed by a second risk score incorporating fasting plasma glucose, would maximize efficiency of identifying people with undiagnosed Type 2 diabetes and those at high risk of future diabetes.

  P. Valensi , F. Extramiana , C. Lange , M. Cailleau , A. Haggui , P. Maison Blanche , J. Tichet and B. Balkau
  Objectives  To evaluate in a general population, the relationships between dysglycaemia, insulin resistance and metabolic variables, and heart rate, heart rate recovery and heart rate variability.

Methods  Four hundred and forty-seven participants in the Data from an Epidemiological Study on the Insulin Resistance syndrome (DESIR) study were classified according to glycaemic status over the preceding 9 years. All were free of self-reported cardiac antecedents and were not taking drugs which alter heart rate. During five consecutive periods: rest, deep breathing, recovery, rest and lying to standing, heart rate and heart rate varability were evaluated and compared by ANCOVA and trend tests across glycaemic classes. Spearman correlation coefficients quantified the relations between cardio-metabolic risk factors, heart rate and heart rate varability.

Results  Heart rate differed between glycaemic groups, except during deep breathing. Between rest and deep-breathing periods, patients with diabetes had a lower increase in heart rate than others (Ptrend < 0.01); between deep breathing and recovery, the heart rate of patients with diabetes continued to increase, for others, heart rate decreased (Ptrend < 0.009). Heart rate was correlated with capillary glucose and triglycerides during the five test periods. Heart rate variability differed according to glycaemic status, especially during the recovery period. After age, sex and BMI adjustment, heart rate variability was correlated with triglycerides at two test periods. Change in heart rate between recovery and deep breathing was negatively correlated with heart rate variability at rest, (r = −0.113, P < 0.05): lower resting heart rate variability was associated with heart rate acceleration.

Conclusions  Heart rate, but not heart rate variability, was associated with glycaemic status and capillary glucose. After deep breathing, heart rate recovery was altered in patients with known diabetes and was associated with reduced heart rate variability. Being overweight was a major correlate of heart rate variability.

  S. Soulimane , D. Simon , J. E. Shaw , P. Z. Zimmet , S. Vol , D. Vistisen , D. J. Magliano , K. Borch-Johnsen and B. Balkau
  Aim  We examined the ability of fasting plasma glucose and HbA1c to predict 5-year incident diabetes for an Australian cohort and a Danish cohort and 6-year incident diabetes for a French cohort, as defined by the corresponding criteria.

Methods  We studied 6025 men and women from AusDiab (Australian), 4703 from Inter99 (Danish) and 3784 from DESIR (French), not treated for diabetes and with fasting plasma glucose < 7.0 mmol/l and HbA1c < 48 mmol/mol (6.5%) at inclusion. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l and/or treatment for diabetes or as HbA1c ≥ 48 mmol/mol (6.5%) and/or treatment for diabetes.

Results  For AusDiab, incident fasting plasma glucose-defined diabetes was more frequent than HbA1c-defined diabetes (PMcNemar < 0.0001), the reverse applied to Inter99 (PMcNemar < 0.007) and for DESIR there was no difference (PMcNema = 0.17). Less than one third of the incident cases were detected by both criteria. Logistic regression models showed that baseline fasting plasma glucose and baseline HbA1c predicted incident diabetes defined by the corresponding criteria. The standardized odds ratios (95% confidence interval) for HbA1c were a little higher than for fasting plasma glucose, but not significantly so. They were respectively, 5.0 (4.1-6.1) and 4.1 (3.5-4.9) for AusDiab, 5.0 (3.6-6.8) and 4.8 (3.6-6.3) for Inter99, 4.8 (3.6-6.5) and 4.6 (3.6-5.9) for DESIR.

Conclusions  Fasting plasma glucose and HbA1c are good predictors of incident diabetes defined by the corresponding criteria. Despite Diabetes Control and Complications Trial-alignment of the three HbA1c assays, there was a large difference in the HbA1c distributions between these studies, conducted some 10 years ago. Thus, it is difficult to compare absolute values of diabetes prevalence and incidence based on HbA1c measurements from that time.

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