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Keywords:

  • phenotypes;
  • genetics;
  • type 2 diabetes;
  • metabolic syndrome;
  • Chinese

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

Background

The aim of this study was to investigate the familiality and clustering of type 2 diabetes (T2DM) and metabolic syndrome (MES) predominantly in families with young-onset diabetes from the Hong Kong Family Diabetes Study.

Methods

One hundred and seventy-nine families (913 subjects) were ascertained through a diabetic proband. Anthropometry, glucose homeostasis, blood pressure and lipid levels were examined. Familial aggregation and inter-relationships of these traits were examined by recurrence risk ratio, heritability, genetic and environmental correlations.

Results

One hundred and forty families (78%) had at least one subject with early-onset T2DM (age-at-diagnosis ≤40 years). MES was highly prevalent in probands (53%) and siblings (25%). Recurrence risk ratios in siblings were high for T2DM (4.3), hypertension (2.9) and central obesity (2.0). Body mass index, waist circumference, blood pressure, plasma insulin, triglyceride, HDL-cholesterol levels, insulin resistance and beta-cell function had high estimates of heritability (0.45–0.63). Bivariate quantitative analyses revealed differential contribution of genetic and environmental factors to the phenotypic correlation between metabolic trait pairs. Obesity indices showed the strongest phenotypic correlation with other traits, and were significantly influenced by genetic factors (genetic correlation = 0.29–0.60).

Conclusion

There was significant familial aggregation of T2DM and related phenotypes including obesity, hypertension and dyslipidaemia. The clustering of metabolic traits is likely due to genetic effects, interacting with shared and unique lifestyle/environmental factors. The high familiality suggests that screening for MES is important, especially in families with young-onset diabetes, and that the families in HKFDS are valuable subjects for genetic studies of these metabolic diseases. Copyright © 2005 John Wiley & Sons, Ltd.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

Diabetes mellitus is now declared a global epidemic 1. Particularly, high prevalence of type 2 diabetes (T2DM) has been observed in non-Caucasian populations such as Asians and American Indians 2, 3. The World Health Organization (WHO) estimated that there would be 40 million diabetic patients in China by 2025 4. Unlike developed countries, where the majority of people with diabetes are >64 years of age, most of the people with diabetes in China are between 45 and 64 years of age 4, 5. Moreover, T2DM is predominant over autoimmune diabetes among young Chinese patients in contrast to that in Caucasians 6, 7.

There is strong evidence that T2DM aggregates in families. In Caucasians, first-degree relatives of diabetic patients had three- to fourfold increased risk for T2DM 8–10. Moreover, monozygotic twins had higher concordance rates for T2DM than dizygotic twins 11, 12. The pathogenesis of T2DM is likely to be polygenic with environmental and/or lifestyle determinants in the majority of patients. In support of this notion, the age-adjusted prevalence rates of diabetes in Chinese were 8.6% in Hong Kong 13, 9.2% in Taiwan 14 and 9.8% in Shanghai 15 compared to 3.5% in Da Qing in rural northeast China 16.

Abnormalities of other metabolic traits including obesity, hypertension, hypertriglyceridaemia and low high-density lipoprotein-cholesterol (HDL-C) are commonly found in T2DM patients and their relatives 17, 18. The coexistence of these risk factors including insulin resistance or glucose intolerance is known as the metabolic syndrome (MES). Its prevalence varies from 10 to 13% in Chinese 15, 19 compared to 20–30% in US whites and Mexican Americans 20, 21, depending on the diagnostic criteria. There is less information about the prevalence of MES in high-risk subjects such as siblings of T2DM patients. Similarly, the degree of clustering of these metabolic traits in families with T2DM and the relative contributions of shared genetic and environmental factors on the phenotypic correlations of these traits remain unclear.

The Hong Kong Family Diabetes Study (HKFDS) commenced in 1998 to examine the interactions between environmental and genetic factors in the development of T2DM and related traits in predominantly families with young-onset diabetes. In this article, we examine the familiality of T2DM and related traits as well as their inter-relationships in a family study of T2DM in the Chinese population of Hong Kong.

Materials and methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

Subjects

Since 1995, data on all patients attending the diabetes clinic of Prince of Wales Hospital (PWH) have been documented for detailed phenotypes and family history 22. Through the PWH diabetes registry, HKFDS ascertained families through a diabetic proband with available first-degree relatives for screening in two stages. First, 161 families were recruited through a diabetic proband with age-at-diagnosis ≤40 years (early-onset group). In the second stage, 30 families were recruited through a diabetic proband with age-at-diagnosis >40 years and with a positive family history (late-onset group). The second cohort was collected to increase the sample size for linkage mapping studies. A total of 191 families with 1066 subjects were recruited. Twelve families were excluded, four had clinical presentation of type 1 diabetes or antibodies to glutamic acid decarboxylase, and eight had either MODY2, MODY3 gene mutations or the mitochondrial DNA nucleotide 3243 A > G mutation. In families with both type 1 and type 2 diabetes, only patients with type 1 diabetes were excluded. In the present study, 913 subjects remained after further exclusion of subjects <16 years old. The probands (n = 179), first- and second-degree relatives (n = 705) and spouses (n = 29) were recruited between January 1998 and March 2002. The family size ranged from 2 to 15 individuals (mean 5.1 and SD 2.3). Informed written consent was obtained for each participating subject. This study was approved by the Clinical Research Ethics Committee of the Chinese University of Hong Kong.

Clinical studies

All subjects attended the study unit after an 8-h overnight fast. Blood pressure (BP), standard anthropometric parameters (weight, height, waist circumference (WC), hip circumference) and body mass index (BMI) were assessed. All subjects completed a questionnaire on demographic data, family and medical histories of cardiovascular risk factors and complications. Sixty-nine percent of parents and 45% of siblings were unavailable and a history of diabetes was obtained from the proband.

Fasting blood samples were collected for measurement of plasma glucose (FPG), insulin (FINS), C-peptide, total cholesterol (TC), triglycerides (TG), HDL-C and renal and liver functions. Glycosylated haemoglobin level was measured for diabetic patients. Early morning spot urine was collected to measure urinary albumin–creatinine ratio after exclusion of urinary tract infection. Subjects with no history of diabetes were tested with a 75-g oral glucose tolerance test (OGTT). Three fasting blood samples collected at 5-min intervals were assessed for mean FPG, FINS and C-peptide. Blood samples were also collected at 15, 30, 60 and 120 min during the OGTT for measurement of plasma glucose and insulin. Insulin resistance was quantified both by the homeostasis model assessment (HOMA%IR) as FINS (µU/mL) × FPG (mmol/L)/22.5 and integrated insulin area under the curve (AUC) using trapezoidal method during OGTT 23–25. Beta cell function (HOMA%β) was assessed as FINS (µU/mL) × 20/(FPG (mmol/L) −3.5.

Clinical definitions

We studied the recurrence risk ratios of various metabolic abnormalities in full siblings by comparison with a previous population survey 26 and used the same clinical definitions. Diabetes and impaired glucose tolerance (IGT) were defined using the 1998 WHO criteria 27. Impaired fasting glucose (IFG) was defined as fasting glucose ≥6.1 mmol/L and <7.0 mmol/L according to the ADA criteria 28. Hypertension was defined as BP ≥140/90 mmHg or treatment with antihypertensive medications. Dyslipidaemia was defined as TC ≥ 6.2 mmol/L, TG ≥ 2 mmol/L, HDL-C <1 mmol/L or LDL-C ≥ 2.5 mmol/L separately. General obesity was defined as BMI ≥ 25 kg/m2 and central obesity as WC >90 cm in men or >80 cm in women using the Asian criteria 29.

The prevalence of MES was also assessed in full siblings using the National Cholesterol Education Program in Adult Treatment Panel III (NCEP III) guideline with Asian criteria for obesity 29. Patients who had at least three of the following five risk factors were considered as having MES: (a) known diabetes or FPG ≥6.1 mmol/L; (b) known hypertension or BP ≥ 130/85 mmHg; (c) TG ≥ 1.7 mmol/L; (d) HDL-C <1.0 mmol/L in men or <1.3 mmol/L in women and (e) WC > 90 cm in men or >80 cm in women.

Statistical analyses

Descriptive continuous data are expressed as mean ± SD or geometric mean, and 95% confidence intervals (CIs). The recurrence risk ratio of diabetes and related phenotypes in siblings (λS) was calculated by dividing the prevalence of disease in siblings by that in the general population at the same age range. The population data was obtained from a community-based population survey on cardiovascular risk factors conducted between 1995 and 1996 26. Age-standardized prevalence rate and λS in the age range 25–44 years were computed by adjustment with the respective population size in this age range. Siblings aged <25 or ≥45 years were not compared because of small sample size. Heritability (h2) of various metabolic traits was expressed as total additive genetic variance over total phenotypic variance, estimated by a maximum-likelihood variance component method implemented in SOLAR (version 2.1.2) 30. Additive genetic (ρG) and environmental (ρE) correlations between pair-wise combination of traits were estimated by a bivariate variance component method implemented in SOLAR 30. The phenotypic correlation (ρP) was expressed as equation image where equation image and equation image are the heritabilities of two traits. ρG and ρE values that are significantly different from zero suggest significant effect of shared genetic (or pleiotropy) and shared environmental factors, respectively, on the phenotypic correlation of two traits. For estimation of trait heritability and pair-wise correlations, data on BP were removed for the 14% of subjects on antihypertensive medications. Similarly, data on FPG, FINS, HOMA%IR, Insulin AUC and HOMA%β were removed for the 68% of diabetic subjects on oral antidiabetic drugs or insulin. Data were transformed by natural logarithm (BMI, WC, systolic BP, TG, HDL-C, HOMA%IR and HOMA%β), square root (diastolic BP, Insulin AUC) or double natural logarithm (FPG) to reduce skewness and kurtosis. Extreme values greater than four standard deviations from the mean were discarded. Data were then standardized to mean zero and unit variance and analysed by variance component method with simultaneous adjustment of covariates including age and sex.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

The present study included 179 families with a total of 913 subjects. These were mostly nuclear families and consisted of 1090 full-sibling pairs, 649 parent–offspring pairs, 67 grandparent–grandchild pairs, 312 avuncular pairs, 10 half-sibling pairs, 3 half-avuncular pairs and 58 first-cousin pairs.

The clinical and metabolic characteristics of all subjects were shown according to their glucose homeostasis status in Table 1. Among all the 913 family members, 15% had IGT or IFG while 38% had T2DM. Among the siblings, 22% had T2DM with 58% being newly diagnosed at screening.

Table 1. The clinical and metabolic characteristics of probands and family members according to glucose homeostasis status in the Hong Kong Family Diabetes Study
 NGTIGT or IFGDiabetes
  • a

    Data presented for diabetic subjects only.

Number431133349
Age (years)36 ± 1345 ± 1347 ± 14
Age-at-diagnosis (years)a 41 ± 13
Sex (male/female)181/25051/82139/210
Body mass index (kg/m2)23.6 ± 4.025.4 ± 4.326.2 ± 4.6
Waist circumference (cm)77 ± 1183 ± 1086 ± 11
Systolic BP (mmHg)118 ± 18131 ± 21133 ± 21
Diastolic BP (mmHg)73 ± 1278 ± 1378 ± 13
Total cholesterol (mmol/L)5.0 ± 1.05.4 ± 1.05.2 ± 1.0
Triglycerides (mmol/L)1.0 (0.9–1.0)1.4 (1.3–1.6)1.5 (1.4–1.6)
HDL-cholesterol (mmol/L)1.4 ± 0.41.3 ± 0.41.3 ± 0.3
LDL-cholesterol (mmol/L)3.0 ± 0.93.3 ± 0.83.2 ± 0.9
HbA1c (%)a 7.5 ± 1.8
Fasting glucose (mmol/L)4.8 ± 0.45.3 ± 0.68.0 ± 3.0
Fasting insulin (pmol/L)46 (44–49)52 (46–58)63 (58–69)
Insulin resistance HOMA%IR1.7 (1.6–1.8)2.0 (1.8–2.3)3.6 (3.2–3.9)
β-cell function HOMA%β123 (115–132)103 (91–116)55 (49–61)

MES was highly prevalent in both probands (53%) and their full siblings (25%). When compared to the general population (Table 2), the siblings had higher risk to develop glucose intolerance with age-standardized λS of 1.5 for IGT and 4.3 for diabetes. Except for low HDL-C, these siblings also had higher risk for hypertension (age-standardized λS = 2.9) and central obesity (age-standardized λS = 2.0). In addition, there were trends of higher λS for these metabolic diseases at younger age-group.

Table 2. Age-specific and age-standardized prevalence and recurrence risk ratio (λS) of siblings for diabetes and related traits
Age groupPrevalence in siblings (%)λS
25–34 (n = 80)35–44 (n = 200)45–54 (n = 104)Age standardized25–3435–4445–54Age standardized
  • a

    λS not available due to lack of population data.

IGT13.820.024.018.51.71.41.41.5
Diabetes8.822.031.719.05.25.13.44.3
Hypertension6.323.140.820.53.63.92.22.9
General obesity31.351.353.443.91.21.71.21.4
Central obesity31.339.745.637.72.62.31.42.0
High cholesterol7.518.520.214.61.82.41.31.8
High triglycerides15.022.519.218.92.11.91.31.8
Low HDL-cholesterol15.015.014.414.90.80.70.60.7
High LDL-cholesterol30.334.952.937.11.31.21.21.2
Metabolic syndromea12.527.533.723.0    

Heritability (h2) of different traits related to metabolic syndrome was estimated in all family members, expressed as the proportion of phenotypic variance explained by additive genetic effect (Table 3). Heritability was high in most metabolic traits including BMI, WC, BP, TG, HDL-C, FINS, HOMA%IR, Insulin AUC and HOMA%β (h2 ranged from 0.45 to 0.63) after simultaneous adjustment for age and sex. However, FPG had relatively low heritability (0.28). The percentage of phenotypic variance explained by age and sex ranged from 1.8% in FINS to 21.7% in systolic BP.

Table 3. Heritability estimates (h2) of diabetes-related phenotypes in all family members from the Hong Kong Family Diabetes Study
Phenotypeh2 (±SE)a% σ2 (covariates)b
  • ln, natural logarithm; sqrt, square root; BMI, body mass index; WC, waist circumference; BP, blood pressure; HDL-C, high-density lipoprotein-cholesterol; HOMA%IR, insulin resistance index; HOMA%β, beta-cell function; Insulin AUC, Insulin area under curve at 0 to 120 min during OGTT.

  • a

    All p-values <0.0001.

  • b

    Percentage of variance of phenotype explained by covariates age and sex.

ln BMI0.60 ± 0.063.8
ln WC0.63 ± 0.0619.4
ln Systolic BP0.55 ± 0.0821.7
sqrt Diastolic BP0.62 ± 0.088.7
ln Triglycerides0.45 ± 0.076.7
ln HDL-C0.63 ± 0.0510.3
lnln Fasting plasma glucose0.28 ± 0.088.0
ln Fasting insulin0.62 ± 0.081.8
ln HOMA%IR0.61 ± 0.082.0
ln HOMA%β0.48 ± 0.085.6
sqrt Insulin AUC0.46 ± 0.080.8

The pair-wise genetic (ρG) and environmental (ρE) correlations of metabolic traits including obesity indices (BMI and WC), BP (systolic and diastolic BPs), lipids (TG and HDL-C), glucose traits (FPG, FINS, HOMA%IR and Insulin AUC) and β-cell function (HOMA%β) were assessed in Table 4. As expected, closely related traits were highly correlated with each other both genetically and environmentally. The genetic correlations for obesity indices were high with lipids and glucose traits (absolute ρG0.29–0.60) but environmental correlations were high with BP, FINS, HOMA%IR and Insulin AUC (ρE0.34–0.55). Lipids demonstrated weak to moderate genetic and environmental correlations with glucose traits. BP and HOMA%β were not significantly correlated genetically and environmentally with most traits. The pair-wise phenotypic correlations (ρP) of these traits reflected the combined effects of ρG and ρE (Table 5). There was moderate correlation between HOMA%IR and Insulin AUC. Obesity indices showed the strongest correlations with all metabolic traits (absolute ρP0.26–0.50) except HOMA%β. However, the correlations among the other traits tended to be weak.

Table 4. Genetic (ρG, upper triangle) and environmental (ρE, lower triangle) correlation matrices among diabetes-related phenotypes in all family members from the Hong Kong Family Diabetes Study
 ln BMIln WCln systolic BPsqrt diastolic BPln TGln HDL-Clnln FPGln FINSln HOMA%IRln HOMA%βsqrt Insulin AUC
  • BMI, body mass index; WC, waist circumference; BP, blood pressure; TG, triglyceride; HDL-C, high- density lipoprotein- cholesterol; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA%IR, insulin resistance index; HOMA%β, beta- cell function, Insulin AUC, Insulin area under curve at 0 to 120 minute during OGTT.

  • a

    p < 0.0005.

  • b

    p < 0.005.

  • c

    p < 0.05.

ln BMI0.92a0.170.180.49a−0.42a0.47c0.40b0.50a0.040.24
ln WC0.86a0.27c0.160.60a−0.50a0.51b0.36b0.46a00.29c
ln Systolic BP0.41b0.34c0.71a−0.01−0.23c0.070.020.07−0.060
sqrt Diastolic BP0.51a0.52b0.66a0.02−0.140.270.35c0.43b0.140.09
ln TG0.27b0.23c0.31b0.33b−0.59a0.270.37b0.38b0.220.34c
ln HDL-C−0.20−0.170.040−0.43a−0.26−0.36b−0.38a−0.16−0.38b
lnln FPG0.22c0.26c0.29c0.060.24c−0.160.40c0.54b−0.120.15
ln FINS0.55a0.55a0.42c0.060.39b−0.090.22c0.99a0.85a0.68a
ln HOMA%IR0.49a0.52a0.42c−0.020.44b−0.130.53a0.90a0.73a0.62a
ln HOMA%β0.31c0.26c0.070.050.060.04−0.65a0.53a0.170.66a
sqrt Insulin AUC0.44a0.40b0.120.140.29c−0.06−0.22c0.39b0.24c0.47a
Table 5. Phenotypic correlation matrix (ρP) among diabetes-related phenotypes in all family members from the Hong Kong Family Diabetes Study
 ln WCln systolic BPsqrt Diastolic BPln TGln HDL-Clnln FPGln FINSln HOMA%IRln HOMA%βsqrt Insulin AUC
  1. BMI, body mass index; WC, waist circumference; BP, blood pressure; TG, triglyceride; HDL-C, high- density lipoprotein- cholesterol; FPG, fasting plasma glucose; FINS, fasting insulin; HOMA%IR, insulin resistance index; HOMA%β, beta-cell function, Insulin AUC, Insulin area under curve at 0 to 120 minute during OGTT.

ln BMI0.890.260.300.38−0.340.310.460.500.170.34
ln WC0.290.280.42−0.380.350.430.480.110.33
ln Systolic BP 0.690.15−0.120.170.160.2000.06
sqrt Diastolic BP  0.16−0.090.140.240.270.100.10
ln TG −0.510.240.370.410.140.31
ln HDL-C −0.20−0.26−0.28−0.07−0.23
lnln FPG 0.280.51−0.43−0.08
ln FINS 0.960.700.53
ln HOMA%IR 0.470.42
ln HOMA%β 0.56

Discussions

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

China is undergoing phenomenal changes in lifestyle and socio-economic development. It has one of the world's largest diabetic populations. People in Hong Kong are mostly Han Chinese whose ancestors originated either locally or migrated from Mainland China during the World War II. Hong Kong has one of the highest prevalence of diabetes (8.6%) and various components of MES (11.6–23.6%) in Asia 13. Against this background, our study presented the genetic epidemiology of T2DM and demonstrated the familial clustering of T2DM and MES traits in a southern Chinese population. Our investigation into the mechanism of development of T2DM and MES in the people of Hong Kong may expectantly help to provide information and rationale for preventing further perpetuation of the disease in China, where people are now living a more affluent and sedentary lifestyle. Our study is particularly relevant to young diabetic patients in Hong Kong and southern China, many of whom remain undiagnosed. Our hospital database demonstrated 30% of diabetic patients were diagnosed before age 40. This proportion may further increase as WHO projects that Asian diabetic population will have a major growth in the middle-age-group in the next two decades 4, 5.

In Caucasians, 3–4 fold increase in prevalence of diabetes has been reported in first-degree relatives, mainly of patients with late-onset T2DM 9, 10, 31. Our study demonstrated similar familial aggregation of diabetes, with age-standardized λS being much higher for diabetes (4.3) than for IGT (1.5). The high λS value for diabetes was partly due to the non-randomness in the ascertainment of high-risk diabetic families for gene-mapping study. Furthermore, since not all parents and siblings are available for examination, either because they were dead, living afar or not interested, it is inevitable that these data could subject to ascertainment bias. However, we also observed higher risk of siblings to develop other metabolic diseases, particularly hypertension and central obesity (λS2.0–2.9). In addition, the λS values for diabetes, hypertension and central obesity were particularly high in young siblings. This clustering of metabolic diseases may not be totally explained by ascertainment bias but may reflect a higher genetic loading of disease genes in young siblings or shared early adverse environment.

The unadjusted prevalence rates of MES were 53% in probands and 25% in siblings in our cohort. Direct comparison of the prevalence rate with other studies is difficult because of the use of different ascertainment and diagnostic criteria. A recent population-based study in Chinese from Shanghai, which had similar prevalence of diabetes compared to Hong Kong (9.8% vs 8.6%), demonstrated a 10% age-adjusted prevalence rate of MES, defined by the coexistence of hyperglycaemia, hypertension and dyslipidaemia 15.

We assessed the contribution of genetic effect to the phenotypic variations of these metabolic traits by heritability estimates. We found that except for FPG, most metabolic traits including obesity, BP, lipids, insulin, Insulin AUC and HOMA indices for insulin resistance and β-cell function, exhibited moderate to high heritability values. A wide range of heritability estimates have been reported for various metabolic traits. In Chinese, several studies have reported moderate to high heritabilities of metabolic traits including BMI (0.39–0.54), TG (0.60), HDL-C (0.63), TG/HDL ratio (0.21–0.34), FPG (0.58) and HOMA%IR (0.46) 32–34. We further analysed the contributions of shared genetic and/or shared environmental factors to the clustering of metabolic traits. Our results demonstrated that obesity was moderately correlated to BP, TG, HDL-C, FPG, FINS, HOMA%IR and Insulin AUC while the inter-relationships amongst these latter traits were weaker particularly with BP. These findings were in accord with the strong predictive value of obesity in the development of diabetes, hypertension and dyslipidaemia 15, 35, and suggest that obesity may be the key link for the common pathogenic pathways. Furthermore, we found that the relative contributions of shared genetic and shared environmental factors to the phenotypic correlation varied for different trait pairs. The phenotypic correlations of BP, especially systolic BP, with other traits were mainly due to shared environmental factors, whereas the correlations of HDL-C with other traits were mainly due to shared genetic factors. Obesity indices tended to have higher genetic correlations than environmental correlations with other traits. These data suggest the existence of common sets of genes that contribute to the clustering of metabolic diseases (pleiotropy) including obesity, glucose intolerance and dyslipidaemia in addition to environmental influence. Similar findings have also been reported in Caucasians 36, 37. Our findings of high heritability values of various metabolic traits reflected the contribution of both shared genetic and environmental factors, and the genetic effects are likely to be overestimated because of ascertainment of high-risk families. However, the inter-relationships of metabolic traits are less likely to be affected by ascertainment bias.

Taken together, our findings are in keeping with the hypothesis regarding the interactions between genetic and environmental/lifestyle factors in the development of MES with variable contributions from both β-cell function decline and increased insulin resistance. Despite strong genetic components of diabetes and its related traits, several large-scale randomized studies have now confirmed the beneficial effects of lifestyle modification with dietary restriction and increased physical activity in preventing the progression from IGT to diabetes 38, 39.

In conclusion, there was strong familial clustering of glucose intolerance, hypertension, dyslipidaemia and obesity in this relatively young cohort of southern Chinese families with T2DM. These data suggests the importance of screening for MES in families with young-onset diabetes. The familiality of the metabolic traits with strong genetic influence makes these families useful for gene mapping studies to dissect the unique and common gene(s) that contribute to these complex diseases. The identification of genetic factors may help to identify at-risk subjects for intensive interventional programs to reduce the clinical manifestation of these metabolic and cardiovascular diseases.

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References

The study was supported by the Research Grant Committee of the Hong Kong Government, Strategic Research Program of the Chinese University of Hong Kong and an educational grant from Armedic Servier Ltd. We thank all medical, nursing and laboratory staff of the Diabetes and Endocrine Centre and Clinical Pharmacology Study Unit at the Prince of Wales Hospital for their dedication and professionalism. Special thanks are extended to late Professor R. C. Turner, Oxford University, UK and late Professor Julian A. J. H. Critchley, the Chinese University of Hong Kong for their inspiration and unfailing support. We are deeply grateful to Professor Nancy J. Cox, Department of Human Genetics, University of Chicago, USA for her expert advice on the genetic analysis.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and methods
  5. Results
  6. Discussions
  7. Acknowledgements
  8. References
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