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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.
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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
| ||NGT||IGT or IFG||Diabetes|
|Age (years)||36 ± 13||45 ± 13||47 ± 14|
|Age-at-diagnosis (years)a|| ||41 ± 13|
|Body mass index (kg/m2)||23.6 ± 4.0||25.4 ± 4.3||26.2 ± 4.6|
|Waist circumference (cm)||77 ± 11||83 ± 10||86 ± 11|
|Systolic BP (mmHg)||118 ± 18||131 ± 21||133 ± 21|
|Diastolic BP (mmHg)||73 ± 12||78 ± 13||78 ± 13|
|Total cholesterol (mmol/L)||5.0 ± 1.0||5.4 ± 1.0||5.2 ± 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 ± 0.4||1.3 ± 0.4||1.3 ± 0.3|
|LDL-cholesterol (mmol/L)||3.0 ± 0.9||3.3 ± 0.8||3.2 ± 0.9|
|HbA1c (%)a|| ||7.5 ± 1.8|
|Fasting glucose (mmol/L)||4.8 ± 0.4||5.3 ± 0.6||8.0 ± 3.0|
|Fasting insulin (pmol/L)||46 (44–49)||52 (46–58)||63 (58–69)|
|Insulin resistance HOMA%IR||1.7 (1.6–1.8)||2.0 (1.8–2.3)||3.6 (3.2–3.9)|
|β-cell function HOMA%β||123 (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 group||Prevalence in siblings (%)||λS|
|25–34 (n = 80)||35–44 (n = 200)||45–54 (n = 104)||Age standardized||25–34||35–44||45–54||Age standardized|
|Metabolic syndromea||12.5||27.5||33.7||23.0|| || || || |
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
|Phenotype||h2 (±SE)a||% σ2 (covariates)b|
|ln BMI||0.60 ± 0.06||3.8|
|ln WC||0.63 ± 0.06||19.4|
|ln Systolic BP||0.55 ± 0.08||21.7|
|sqrt Diastolic BP||0.62 ± 0.08||8.7|
|ln Triglycerides||0.45 ± 0.07||6.7|
|ln HDL-C||0.63 ± 0.05||10.3|
|lnln Fasting plasma glucose||0.28 ± 0.08||8.0|
|ln Fasting insulin||0.62 ± 0.08||1.8|
|ln HOMA%IR||0.61 ± 0.08||2.0|
|ln HOMA%β||0.48 ± 0.08||5.6|
|sqrt Insulin AUC||0.46 ± 0.08||0.8|
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 BMI||ln WC||ln systolic BP||sqrt diastolic BP||ln TG||ln HDL-C||lnln FPG||ln FINS||ln HOMA%IR||ln HOMA%β||sqrt Insulin AUC|
|ln Systolic BP||0.41b||0.34c||—||0.71a||−0.01||−0.23c||0.07||0.02||0.07||−0.06||0|
|sqrt Diastolic BP||0.51a||0.52b||0.66a||—||0.02||−0.14||0.27||0.35c||0.43b||0.14||0.09|
|sqrt Insulin AUC||0.44a||0.40b||0.12||0.14||0.29c||−0.06||−0.22c||0.39b||0.24c||0.47a||—|
Table 5. Phenotypic correlation matrix (ρP) among diabetes-related phenotypes in all family members from the Hong Kong Family Diabetes Study
| ||ln WC||ln systolic BP||sqrt Diastolic BP||ln TG||ln HDL-C||lnln FPG||ln FINS||ln HOMA%IR||ln HOMA%β||sqrt Insulin AUC|
|ln Systolic BP|| ||—||0.69||0.15||−0.12||0.17||0.16||0.20||0||0.06|
|sqrt Diastolic BP|| || ||—||0.16||−0.09||0.14||0.24||0.27||0.10||0.10|
|ln TG|| ||—||−0.51||0.24||0.37||0.41||0.14||0.31|
|ln HDL-C|| ||—||−0.20||−0.26||−0.28||−0.07||−0.23|
|lnln FPG|| ||—||0.28||0.51||−0.43||−0.08|
|ln FINS|| ||—||0.96||0.70||0.53|
|ln HOMA%IR|| ||—||0.47||0.42|
|ln HOMA%β|| ||—||0.56|
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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.
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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.