Q J Med 2000; 93: 183-190
© 2000 Association of Physicians
Glycaemic control in type 2 diabetes: the impact of body weight, ß-cell function and patient education
From the Diabetes and Endocrine Center, Department of Medicine and Therapeutics, Prince of Wales Hospital, Chinese University of Hong Kong, Hong Kong
Received 19 March 1999 and in revised form 11 January 2000
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Summary |
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 Top Summary IntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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We examined the determinants of glycaemic control in a consecutive cohort of 562 newly-referred Chinese type 2 diabetic patients (57% women) during a 12-month period. All patients underwent a structured assessment with documentation of clinical and biochemical characteristics. Pancreatic ß-cell function was assessed by fasting plasma C-peptide concentration. Insulin deficiency was defined as fasting plasma C-peptide <0.2 pmol/ml. Insulin resistance (IR) was calculated using the homeostasis model assessment (HOMA) based on a product of fasting plasma glucose and insulin concentrations. Treatment was considered appropriate when insulin-deficient patients were treated with insulin and non-insulin-deficient patients were treated with oral agents or diet. Mean (±SD) age was 54.3±13.8 years (range 17–87 years) and disease duration was 5.0±5.9 years. At the time of referral, 70.5% (n=396) were on drug therapy (9% on insulin and 62.8% on oral agents), 20.6% (n=116) were on diet and 9% (n=50) had not received any form of treatment. The mean HbAlc was 8.4±2.3%. The geometric mean (x/÷ antilog SD) of IR was 4.62x/÷2.51 (range 0.63–162.7) and correlated only with waist : hip ratio (WHR, p=0.008). The geometric mean of plasma C peptide was 0.47x/÷2.89 nmol/l and correlated with BMI (p<0.001). Glycated haemoglobin was correlated positively with age (p=0.013), disease duration (p<0.001), IR (p<0.001) and negatively with BMI (p<0.001). Glycated haemoglobin was lower in patients who had seen a dietitian (7.9% vs. 8.7%, p<0.001) or diabetes nurse (7.8% vs. 8.7%, p<0.001) or who performed self blood glucose monitoring (7.9% vs. 8.6%, p=0.001) and higher among smokers (8.9% vs. 8.2%, p=0.003). Compared to insulin-deficient patients (n=118), non-insulin-deficient patients (n=413) had features resembling that of the Metabolic Syndrome with increased WHR (p=0.005), blood pressure (p<0.001), BMI (p=0.001) and were older (p=0.04). Amongst the insulin-deficient patients, 27% were treated with oral agents or diet. Patients receiving appropriate therapy (n=362) had a lower HbAlc than those treated inappropriately (n=173) (8.2% vs. 8.7%, p=0.02). On multivariate analysis, short disease duration (p<0.001), low IR (p<0.001), high BMI (p=0.001), diabetes education (p<0.001), lack of smoking (p=0.014) and choice of appropriate treatment (p=0.009) were the independent determinants of good glycaemic control.
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Introduction |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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The importance of glycaemic control in the prevention of microvascular complications has been confirmed in both type 1 and type 2 diabetic patients.1,2 However, achievement of optimal glycaemic control remains a major challenge to health-care providers. In the Diabetes Control and Complication Trial (DCCT) where patients were closely monitored, only 44% of patients in the intensively-treated group achieved the goal of a glycosylated haemoglobin
6.05% at least once. Identification of factors that determine glycaemic control may help health-care providers to manage these high-risk patients more effectively. We examined the relationships between glycaemic control and some potential determining factors, with particular emphasis on therapeutic patient education, self-monitoring, treatment regimen, insulin resistance and pancreatic ß-cell function.
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Methods |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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This was a cross-sectional study conducted in the regional diabetes clinic at the Prince of Wales Hospital (PWH) of Hong Kong, a teaching hospital with a catchment population of 1.2 million, and typical social class representation. A total of 562 consecutive newly-referred type 2 diabetic patients, defined according to the 1985 WHO criteria,3 were studied during a 12-month period between January and December 1996. Patients with acute type 1 presentation, including diabetic ketoacidosis and heavy ketonuria were excluded. Classification of diabetes was done by the authors, who were all endocrinologists, and was based on clinical presentation, presence of ketonuria, and history of diabetic ketoacidosis. All patients were referred from clinics in the community or other hospital clinics or were discharged from the hospital wards. Due to the lack of a comprehensive health-care financing policy in Hong Kong, the majority of patients with chronic diseases were followed up at the low-cost public hospital clinics.
Since 1993, the PWH Diabetes Centre has adopted the EuroDiab Database4 and designed a structured assessment with data computation for all newly-referred diabetic patients to streamline management.5 Documentation included demographic data, body weight and height, hip and waist circumferences, past medical history, tobacco and alcohol intake, previous education by dietitians and diabetes nurses, and methods of self-monitoring. Body mass index was calculated as the weight (kg) divided by the square of the height (m). Waist : hip ratio was defined as waist circumference divided by hip circumference. Blood pressures were measured in both arms, after the patients had rested for at least 5 min, with a mercury sphygmomanometer. The Korotkoff sound V was taken as the diastolic blood pressure. The mean of two readings measured 1 min apart was used. The higher reading in either arm was taken as the final reading.
Patients were instructed to fast for at least 8 h and not to take any medication or insulin injections on their first clinic visit. Venous blood was sampled for measurement of fasting plasma glucose (FPG), glycosylated haemoglobin (HbAlc), plasma creatinine, lipid profile including triglyceride (TG), total cholesterol (TC) and high-density cholesterol (HDL-C) as part of the routine assessment. An extra 10 ml of blood was taken for C-peptide and insulin measurement, with informed consent.
Insulin resistance (IR) was estimated using the homeostasis model assessment (HOMA) as a product of FPG (mmol/l) and insulin concentration (µU/ml) divided by 22.5.6 Insulin deficiency was defined as a fasting C-peptide 0.2 nmol/l.7,41 The use of insulin was considered to be appropriate in insulin-treated patients with a fasting plasma C-peptide 0.2 nmol/l or those with no insulin treatment with C-peptide >0.2 nmol/l. Patients were considered to receive inappropriate treatment if they were not treated with insulin but had a plasma C-peptide 0.2 nmol/l or were insulin-treated, but had a plasma C-peptide >0.2 nmol/l.
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Laboratory assays |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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Plasma glucose was measured by a glucose oxidase method (Diagnostic Chemicals). HbAlc was measured by an automated ion-exchange chromatographic method (Bio-Rad) (normal range 5.1–6.4%). Plasma C-peptide was measured by radioimmunoassay (Novo Nordisk) with an intra-assay coefficient of variation (CV) of 3.4% and inter-assay CV of 9.6%. The lowest detection limit was 0.1 nmol/l. Insulin assay was performed using a radioimmunoassay kit (Pharmacia). The lower limit of detection was <2 µU/ml. The inter-assay CV was 5%. Total cholesterol and TG were measured enzymically in plasma with commercial reagents (Dimension, Dupont Instrument). HDL-C was measured by the same enzymic assay after precipitation of the HDL-C by the heparin-manganese method. LDL-C was calculated using Friedewald's equation.8
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Statistical analysis |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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Statistical analysis was done using the Statistical Package for Social Sciences (SPSS) software. Plasma triglyceride, C-peptide, insulin level and HOMA were logarithmically transformed due to skewed distributions. The Student's t-test and
2 tests were used for comparison between groups as appropriate. Pearson's correlation analysis was used to examine the relationships between variables. Linear regression and logistic regression analysis were used to examine the independent relationships amongst variables. All results are expressed as means ±SD or geometric mean x/÷ antilog SD (for log-transformed data). A p value <0.05 was considered statistically significant. |
Results |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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Baseline clinical and biochemical characteristics
Among the 562 patients (54.3±13.8 years, range 17–87 years), 242 (43.1%) were men (54.9±12.8, range 19–82 years) and 320 (56.9%) were women (53.9±14.5, range 17–87 years). The mean HbAlc was 8.4±2.3% and there was no gender difference. The distribution of glycosylated haemoglobin is shown in Figure 1
. Only 55.3% of patients had HbAlc <8% upon referral, and a significant proportion of patients (22.8%) had very poor glycaemic control with HbAlc>10%. Table 1 summarizes the clinical and biochemical characteristics between men and women. There was a higher prevalence of tobacco and alcohol intake in men than in women. Women were more likely to have seen a dietitian or diabetes nurse. Men had a lower BMI and HDL-C but higher WHR than women.
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Pancreatic ß-cell function and insulin secretion
Of the 562 patients, 31 samples of plasma were not measured for C-peptide due to non-availability or technical errors. Of the remaining 531, the geometric mean plasma C peptide was 0.47x/÷2.89 nmol/l and correlated with BMI (p<0.001). Based on the fasting plasma C peptide, patients were divided into insulin-deficient (0.2 nmol/l, 22.2%, n=118) and non-insulin-deficient groups (>0.2 nmol/l, n=413).7 The non-insulin-deficient patients were older, more obese and had higher BP, plasma TG and lower HDL-C concentrations than the insulin-deficient group (Table 2). In a logistic regression model (model fitness =78.49%, 2=48.30, R2=0.089, p<0.001) using age, BMI, WHR, systolic and diastolic BP, TG and HDL-C as independent variables, a non-insulin-deficient state (yes=1; no=2) was independently predicted by increased BMI (p=0.006; ß=0.115), TG (p=0.042, ß=0.390) and diastolic BP (p=0.012, ß=0.037).
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Determinants of glycaemic control
Of these 562 patients, 16.9% were non-smokers, 79.0% did not drink alcohol, 35.7% had received therapeutic education from dietitians and 30.9%, from a diabetes nurse, while 25.0% performed self blood glucose monitoring (SBGM). At the time of referral, 70.5% (n=396) were on drug therapy (9% on insulin and 62.8% on oral agents), 20.6% (n=116) were on diet and 9% (n=50) had not received any form of treatment. The latter had only visited their family doctors once and referred to our hospital for further management after a mean waiting list of 4 months. HbAlc was positively correlated with age (p=0.013, r=0.105), duration of disease (p<0.001; r=0.231), and IR (as indicated by HOMA) (p<0.001; r=0.269) but inversely correlated with BMI (p<0.001, r=-0.171). Insulin resistance correlated only with waist hip ratio (WHR, p=0.008), but not with BMI. HbAlc was significantly lower in subjects who had received education from dietitian (7.9% vs. 8.7%; p<0.001) or diabetes nurse (7.8% vs. 8.7%; p<0.001); who had never smoked (8.2% vs. 8.9%; p=0.003), who performed SBGM (7.9% vs. 8.6%; p=0.001) and in those who received appropriate insulin treatment for their insulin deficient state (8.2% vs. 8.8%; p=0.021) as compared to patients who did otherwise.
We used a linear regression model with age, duration of disease, BMI, HOMA, previous education by dietitian or diabetes nurse, SBGM, smoking and appropriateness of insulin treatment as independent variables to predict HbAlc (R2=0.190, F=20.36, p<0.001). Duration of disease (p<0.001; ß=0.079), HOMA (p<0.001, ß=0.637), BMI (p=0.001; ß=-0.084), diabetes education (p<0.0001, ß=0.870), smoking history (p=0.014, ß=-0.486) and choice of treatment appropriate to their insulin secretory status (p=0.009, ß=0.533) were all independent determinants of HbAlc.
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Discussion |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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The DCCT and United Kingdom Prospective Diabetes Study (UKPDS) trials have settled the long-standing debate over the importance of glycaemic control on the development of microvascular complications in both type 1 and type 2 diabetes.1,2 Our prospective study in Chinese patients also found fasting plasma glucose to be an independent predictor for cardiovascular and renal deaths.9 There is also increasing evidence showing that chronic hyperglycaemia can cause direct tissue damage by altering intracellular signalling pathways and endothelial functions.10,11 Despite this experimental and clinical evidence, the challenge faced by the majority of health-care providers lies in how to achieve optimal glycaemic control effectively.
Type 2 diabetes is a heterogeneous disease with varying degrees of insulin resistance and deficiency.12 There is considerable overlap between type 1 and type 2 diabetes in clinical presentations, especially in some non-Caucasian populations.13,14 The large number of exogenous and lifestyle factors which influence glycaemia often make optimal diabetes management difficult.14
Although the Prince of Wales Hospital is a regional teaching hospital, the majority of patients are referred from the community for financial reasons rather than severity of disease. Due to the lack of a long-term health-care financing policy in Hong Kong, the majority of people do not have medical insurance cover. As a result, patients with chronic diseases like diabetes, including those from the middle classes, often seek medical treatment in public hospital clinics where they pay a nominal fee.
In the present study involving 562 newly-referred type 2 diabetic patients, the mean HbAlc was 8.4%. In the DCCT, the risk of diabetic complications increased sharply once HbAlc rose beyond 8% and exponentially when it exceeded 10%.1 In this cohort, 44.6% patients had HbAlc >8% and 22.8% had HbAlc >10%. Less than 31.1% of patients had HbAlc within the optimal range (<7%). Similar findings have been reported in other hospital-based studies conducted in Western countries.15–17
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Therapeutic patient education |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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Therapeutic patient education and self care are essential for effective diabetes management.18,19 In this cohort, only 35.7% of patients had seen a dietitian, 30.9% had seen a diabetes nurse, and only 25% of subjects performed SBGM. The majority of these patients had received diabetes education either during hospital admissions or when they attended other hospital clinics before being referred to the Prince of Wales Hospital diabetes clinic. Nearly all patients newly referred from the community had not seen a dietitian or diabetes nurse before.
A diabetes educator aims to modify patients’ behavior by providing knowledge, changing attitude and teaching skills with particular emphasis on self care. It can be a slow and painstaking process.20 Nevertheless, in the present study, we were able to demonstrate a correlation between therapeutic patient education and SBGM, and glycaemic control. Non-smokers also had a lower HbAlc than smokers. Tobacco has been shown to be a predictor for subsequent development of diabetes in middle-aged men.21 In both type 1 and type 2 patients, in-patient education programmes22 and outpatient counselling improve glycaemic control,22–25 the more so in those who received intensive education programms.24 Simple educational tools such as flash cards have also been shown to be of benefit in Asian diabetic patients living in England.26 The most impressive study in this respect was the dramatic reduction in leg amputation after education by a diabetes educator in a country hospital setting.15 Taken together, our findings suggest that patient education and self care could have a beneficial effect on glycaemic control.
Meal planning and dietary modifications are important aspects of diabetes education.24 In type 1 diabetes, there were close associations between careful meal planning and HbAlc.27 Type 2 diabetic patients who had received dietary instructions also showed an improvement in glycaemic control, irrespective of the methods of instruction.28 On the other hand, type 2 diabetic patients who perceived absence of dietary recommendation had poor glycaemic control.29
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Self blood glucose monitoring (SBGM) |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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The continuous improvement in technology has made SBGM more user-friendly. Although early studies in the 70s supported the beneficial effects of SBGM on glycaemic control,30,31 these findings were not always consistent.32–35 Some of these effects have been attributed to increased education and medical care rather than SBGM itself.36 In the present study, HbAlc was 0.8% lower in patients who performed SBGM than in those who did not. Although this improvement might be related to other aspects of diabetes education received during the process of teaching SBGM, it is conceivable that the feedback received during self monitoring further improved lifestyle modification.
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Insulin resistance and deficency |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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In agreement with most studies,37,38 non-insulin-deficient patients in the present study had features resembling that of the metabolic syndrome. Increased plasma C peptide concentration was associated with clustering of cardiovascular risk factors, including high blood pressure, obesity (central and general) and dyslipidaemia characterized by low HDL-C and high triglycerides.
In Caucasians, 95% of type 2 diabetic patients are non-insulin-deficient.39 By contrast, as many as 21% of these newly-referred Chinese type 2 diabetic patients had low plasma C peptide concentration suggesting insulin deficiency. Although insulin resistance is a feature of type 2 diabetes, insulin deficiency is an important factor in its pathogenesis.40 These findings are in accordance with our previous report where 25% of a separate cohort of type 2 diabetic patients attending the diabetes clinic were treated with insulin, despite only 3% having acute type 1 presentation.16 In another cohort of young type 2 diabetic patients, we found a high prevalence of insulin deficiency41,42 but low prevalence of autoimmunity. Similar findings have also been found in Chinese patients with classical type 1 presentation.43
In Caucasians, the clinical mode of presentation often predicts the insulin status in diabetic patients, although not without difficulty at times.44,45 Latent autoimmune diabetes in the adult,46 genetic causes40 and disease duration29,47 all have effects on insulin secretory function. In the present study, increased BMI, triglycerides and diastolic blood pressure were the major predictors for a non-insulin-deficient state in our logistic regression model. Other the other hand, based on plasma C-peptide, 21% of patients had insulin deficiency, although only 9% were treated with insulin. More importantly, we found that patients receiving appropriate insulin treatment had better glycaemic control than those not receiving appropriate therapy. In the UKPDS, early use of insulin in type 2 diabetic patients did not confer additional advantage over oral agents, but increased the risk of hypoglycaemia.48 Based on these findings, plasma C-peptide may be a useful indicator of insulin secretory function to optimize therapy. In this respect, fasting plasma C-peptide has been shown to predict future insulin requirement in type 2 patients.39,49 Similarly, insulin therapy can be stopped in patients who have adequate post-glucagon C-peptide response.50,51
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Body mass index versus waist:hip ratio |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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In Caucasian patients with type 2 diabetes, obesity is associated with poor glycaemic control.51 However, only 7.8% of patients in this study were obese using the conventional definition of BMI
30 kg/m2. Furthermore, we found a negative correlation between HbAlc and BMI in this Chinese population. Recent studies comparing south Asian and European type 2 diabetic patients also found a lower BMI in the former group.14 In Korean type 2 diabetic patients, non-obese patients had a lower fasting serum C-peptide, more pronounced weight loss from time of maximal weight and higher percentage of insulin treatment than the obese patients. In these Korean patients, there were also close associations between fasting plasma C-peptide and BMI.53 In a Swedish study, lean type 2 diabetic patients had impaired insulin secretion rather than insulin resistance.54 In Indian type 2 diabetic patients, lean patients had higher HbAlc and fasting plasma glucose than obese patients.55 In the UKPDS, patients who failed dietary treatment also had low BMI.48 Taken these findings together, a low BMI which reflects underlying insulin deficiency, is of particular relevance in Asian including Chinese patients. Our findings also suggest that insulin deficiency is an important factor contributing to poor glycaemic control, especially in the event of non-insulin therapy. As in other studies,56 we found close associations between HOMA (a marker of insulin resistance), WHR and HbAlc in our Chinese patients with increased WHR contributing to poor glycaemic control. The other determinant of glycaemic control was duration of disease. Based on the UKPDS, the latter is probably due to progressive deterioration in pancreatic ß-cell function as shown in both type 157 and type 2 diabetic patients.29,44,58 Furthermore, the UKPDS has shown that pancreatic ß function failed at an annual rate of 3–4% in type 2 diabetic patients, so that an increase in dosage of oral agents or changeover to insulin treatment was required in the majority of type 2 patients.48
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Conclusions |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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Our findings confirm the heterogeneity of pancreatic ß-cell functions in Chinese type 2 diabetic patients. Long duration of disease, a low BMI (suggesting insulin deficiency), increased WHR (suggesting insulin resistance), lack of therapeutic patient education, inappropriate treatment in relation to pancreatic ß-cell function and smoking habit were all independent determinants for poor glycaemic control. We also demonstrated the potential beneficial effects of SBGM and education by a dietitian on glycaemic control in these patients.
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Acknowledgments |
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We thank all nursing and clerical staff at the PWH Diabetes Center for the documentation and computation of the database. Special thanks are extended to Mr Kevin HM Yu for designing the computer program for the database. We thank Mr Stanley Ho and Mr Cheung Wah for their technical assistance and for performing the insulin and C peptide assays.
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Notes |
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Address correspondence to Dr W.B. Chan, Department of Medicine and Therapeutics, Prince of Wales Hospital, Chinese University of Hong Kong, Shatin NT, Hong Kong
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References |
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TopSummaryIntroductionMethodsLaboratory assaysStatistical analysisResultsDiscussionTherapeutic patient educationSelf blood glucose monitoring...Insulin resistance and deficencyBody mass index versus...ConclusionsReferences |
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