Familial concordance of metabolic syndrome in Korean population—Korean National Health and Nutrition Examination Survey 2005

diabetes research and clinical practice 93 (2011) 430–436

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Diabetes Research and Clinical Practice jou rnal hom ep ag e: w ww.e l s e v i er . c om/ loca te / d i ab r es

Familial concordance of metabolic syndrome in Korean population—Korean National Health and Nutrition Examination Survey 2005 Myung Ha Lee a, Hyeon Chang Kim a,c,d,*, G. Neil Thomas b, Song Vogue Ahn a, Nam Wook Hur a, Dong Phil Choi a, Il Suh a a

Department of Preventive Medicine, Yonsei University College of Medicine, 250 Seongsanno, Seodaemun-Gu, Seoul 120-752, South Korea Department of Public Health, Epidemiology and Biostatistics, University of Birmingham, Birmingham, UK c Severance Institute for Vascular and Metabolic Research, Yonsei University College of Medicine, Seoul, South Korea d Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA b

article info

abstract

Article history:

Aims: To investigate the familial concordance of metabolic syndrome and its components

Received 1 December 2010

in a nationally representative survey in Korean.

Accepted 2 June 2011

Methods: We used data from the Korean National Health and Nutrition Examination Survey

Published on line 5 July 2011

(KNHANES), a nationwide survey examining the general health and nutritional status of the

Keywords:

Results: Prevalence of the metabolic syndrome was 17.1% for husbands, 11.7% for wives, 14.3%

Family

for parents, and 7.2% for offspring. After adjustment for age, there were strong positive

Korean people. We enrolled 1641 married couples and 1527 parents–1342 offspring.

Concordance

correlations between family members for the metabolic variables. Compared with husbands

Metabolic syndrome

whose wives did not have metabolic syndrome, adjusted odds ratio in husbands whose wives had metabolic syndrome was 1.43 (95% CI: 1.10–1.87) for the risk of having metabolic syndrome. Similarly, wives whose husbands had metabolic syndrome had 1.41 (95% CI: 1.08–1.84) times higher risk of having metabolic syndrome. Compared with children whose parents did not have metabolic syndrome, adjusted odds ratio in children with at least one parent with the metabolic syndrome was 2.56 (95% CI: 1.09–5.98) for the metabolic syndrome. Conclusions: Our study revealed that there is significant familial concordance for metabolic syndrome and its components in Korean families. # 2011 Elsevier Ireland Ltd. All rights reserved.

1.

Introduction

Metabolic syndrome is defined by a clustering of metabolic abnormalities such as central obesity, dyslipidemia, hypertension and hyperglycemia. These features of metabolic syndrome are already well-known independent risk factors for diabetes and/or cardiovascular diseases [1,2]. Metabolic syndrome is associated with an increased risk of all-cause and cardiovascular mortality in adults [3,4]. Epidemiologic data from various

regions have shown familial correlations of cardiovascular risk factors and metabolic syndrome features [5–7]. Environmental factors, such as dietary and physical inactivity, are important etiological determinants of metabolic syndrome [8] that contribute to disease independently or in conjunction with genetic components. Familial similarities resulting from an interaction between shared environment and genetic background highlight the importance of these factors on the metabolic profile [9,10]. Throughout a child’s life, parents are an influential factor due to their genetic and

* Corresponding author. Tel.: +82 2 2228 1873; fax: +82 2 392 8133. E-mail address: [email protected] (H.C. Kim). 0168-8227/$ – see front matter # 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.diabres.2011.06.002

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diabetes research and clinical practice 93 (2011) 430–436

environmental contributions. Metabolic parameters, including blood pressure, weight, lipid profile, glucose and insulin metabolism, have a genetic base and exhibit familial clustering [11,12]. Spousal concordance is a useful means of describing the impact of shared lifestyle and socioeconomic environment independent of genetic influences [13,14]. To further examine the contribution of environmental factors to metabolic syndrome in Korea, we studied the condition of familial concordance and its components in a nationally representative survey.

2.

Methods

2.1.

Study population

This study was based on the data obtained from the Korean National Health and Nutrition Examination Survey III (KNHANES III) which was a nationwide survey examining the general health and nutritional status of Korean people. The surveys were conducted between April 6 and June 18, 2005. The KNHANES utilized four different surveys: a health interview, a health behavior survey, a health examination, and a nutrition survey. In the KNHANES III, 42,780 individuals from 600 districts were sampled in consideration of location and residence-type in order to establish full representativeness of the whole nation. All 600 sampling districts undertook the health interview survey, however only 200 districts (one third

random samples) were subject to the health examination, behavior, and nutrition surveys. These 200 districts were used for the evaluation of our study. There were 6510 adults and adolescents aged 10 and over who completed the health examination. Among them, 1641 married couples and 1527 parents–1342 offspring were identified. Details of the survey methods have been reported previously [15].

2.2.

Health examination survey and interview survey

Height and weight were obtained using standardized techniques and equipment. Body mass index was determined as weight (kg) divided by height (m) squared. Waist circumference was measured midway between the inferior margin of the last rib and the iliac crest in a horizontal plane. Blood pressure was measured on the right arm using a standard mercury sphygmomanometer after 5 min rest. Three measurements were taken: the mean of the second and third measurements was used in the analysis. Blood samples were collected in the morning after fasting for at least 8 h and analyzed in the certified central laboratory on the same day. Serum total cholesterol, triglycerides, HDL-cholesterol and fasting glucose levels were measured by automated enzymatic techniques, LDL-cholesterol was calculated using the Friedewald’s formula [16] in individuals with triglycerides <400 mg/dl. Demographic characteristics, family history of disease, smoking habits, alcohol consumption, physical activity, and dietary factors were collected from the health interview and nutrition surveys.

Table 1 – Characteristics of study subjects.

Age (years) Weight (kg) Waist circumference (cm) Body mass index (kg/m2) Fasting blood glucose (mg/dl) HbA1c (%) Total cholesterol (mg/dl) LDL-cholesterol (mg/dl) HDL-cholesterol (mg/dl) Triglyceride (mg/dl) Systolic blood pressure (mm Hg) Diastolic blood pressure (mm Hg) Total energy intake (kcal) Protein intake (g) Fat intake (g) Sugar intake (g) Smoking Drinking Exercise Metabolic syndrome Each metabolic risk factor Waist circumference Triglyceride HDL-cholesterol Blood pressure Fasting blood glucose

Husband

Wife

N = 1641

N = 1641

50.1  12.9 68.9  10.4 85.2  8.5 24.2  3.1 98.9  23.3 7.2  1.4 187.5  34.7 114.8  30.7 41.8  10.3 171.2  172.4 123.3  15.7 81.0  10.1 2355.9  23.3a 91.3  1.2a 47.8  1.0a 354.7  3.3a 1302 (40.7) 1340 (41.8) 825 (25.8) 539 (17.1)

46.7  12.4 58.3  8.4 79.0  9.1 23.8  3.3 93.0  17.9 7.6  1.7 185.9  35.9 116.3  31.1 46.7  10.8 116.7  77.0 115.8  17.7 75.1  10.1 1850.9  17.8a 71.0  0.9a 36.7  0.7a 302.0  2.8a 60 (1.9) 1075 (33.6) 782 (24.4) 371 (11.7)

<.0001 <.0001 <.0001 <.001 <.0001 0.030 0.187 0.178 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 <.0001 0.039 <.0001

413 344 1037 459 279

0.015 <.0001 <.0001 <.0001 <.0001

476 652 760 791 492

(14.6) (20.4) (23.8) (24.1) (15.5)

Data are expressed as mean  SD or number (%). Standard error.

a

(12.6) (10.8) (32.5) (14.0) (8.8)

p-Value

Parent

Offspring

N = 1527

N = 1342

47.1  8.3 63.6  10.3 81.8  8.9 24.1  3.0 96.2  22.0 7.4  1.8 188.2  34.6 116.5  29.6 44.6  10.2 145.9  139.1 117.9  16.2 78.4  10.7 2073.9  22.5a 81.4  1.1a 43.2  0.9a 317.6  3.2a 566 (28.5) 1158 (45.5) 791 (31.0) 409 (14.3)

18.5  7.5 56.8  13.9 71.6  10.0 21.3  3.6 87.4  16.3 8.2  5.2 161.7  28.8 97.2  23.8 45.7  9.9 94.9  59.7 107.4  11.7 69.8  10.0 2118.4  25.4a 78.1  1.1a 55.9  1.1a 313.4  3.5a 152 (7.7) 595 (23.4) 484 (19.0) 207 (7.2)

381 466 866 533 372

(13.3) (16.2) (30.2) (18.6) (13.0)

148 259 682 786 46

(5.2) (9.0) (23.8) (27.4) (1.6)

p-Value <.0001 <.0001 <.0001 <.0001 <.0001 0.716 <.0001 <.0001 0.006 <.0001 <.0001 <.0001 0.189 0.037 <.0001 0.376 0.002 <.0001 <.001 <.0001 <.0001 <.0001 0.002 <.0001 <.0001

432

(1.11–1.83) (0.85–1.44) (0.82–1.29) (0.94–1.62) (0.79–1.41) b

a

Odds ratio of having metabolic syndrome or its component in one spouse whose partner had same abnormality, compared with those whose partner did not. Adjusted for age, spousal age, smoking status, alcohol consumption, exercise status, stress intensity.

1.42 1.11 1.03 1.23 1.06 (1.09–1.79) (0.88–1.47) (0.83–1.31) (0.93–1.60) (0.79–1.40) 1.40 1.14 1.04 1.22 1.05 162 125 346 170 100 386 330 994 418 266 (1.03–1.79) (1.13–1.93) (0.84–1.43) (1.10–1.88) (0.92–1.59) 1.36 1.48 1.10 1.44 1.21 (1.00–1.73) (1.06–1.78) (0.79–1.32) (1.10–1.86) (0.92–1.57) 126 156 168 224 146 446 629 729 732 476

1.32 1.38 1.02 1.43 1.20

1.41 (1.08–1.84) 1.40 (1.08–1.82) 1.43 (1.10–1.87) 1.36 (1.05–1.77) 153 523

Metabolic syndrome Each metabolic risk factor Waist circumference Triglyceride HDL-cholesterol Blood pressure Fasting blood glucose

Age-adjusted

Multivariate-adjustedb

359

153

Multivariate-adjustedb

No. of people whose husbands had same abnormality No. of people Odds ratio (95% CI)

No. of people whose wives had same abnormality

The prevalence of metabolic syndrome was observed to be 17.1% for husbands (mean age = 50.1  12.9), 11.7% for wives (mean age = 46.7  12.4), 14.3% for parents (mean age = 47.1  8.3), and 7.2% for offspring (mean age = 18.5  7.5). High blood pressure (24.1%) was the most frequent metabolic abnormality in husbands, while low serum HDL-cholesterol (32.5%) was the most frequent in wives. Additionally, high blood pressure (27.4%) was the most frequent in offspring (Table 1). Table 2 shows the odds ratios of having metabolic syndrome or its components in one spouse whose partner had the same abnormality, compared to those whose partner did not have the abnormality. The multivariate-adjusted odds ratio in husbands whose wives had metabolic syndrome was 1.43 (95% CI: 1.10–1.87), compared with husbands whose wives

No. of people

Results

a

Characteristics of the study participants were displayed as mean values with standard deviations for continuous variables or numbers with percent for categorical variables. Standard t-test and x2-test were used to compare differences between family members. Familial concordance of metabolic syndrome and each metabolic component was measured, and described using odds ratio and their respective 95% confidence intervals. Marriage duration was estimated by time span between wife’s age at her first childbirth and her current age. Familial intraclass correlation coefficient of metabolic variables and dietary factors between spouse, parents–offspring, and siblings was estimated. Statistic analyses were performed using the SAS version 9.2 (SAS Institute, Cary, NC, USA) and p < 0.05 was considered to be statistically significant.

3.

Wife

Statistical analysis Table 2 – Spousal concordance of metabolic syndrome and its each metabolic risk factor.

2.4.

Odds ratioa (95% CI)

Metabolic syndrome in adults was defined according to the ‘‘harmonizing the metabolic syndrome’’ criteria [17] as having 3 or more of the following: (1) blood pressure 130/85 mm Hg or antihypertensive medication; (2) fasting glucose 100 mg/dl (recommended by American Diabetes Association, 2003) or diabetes treatment (insulin therapy or oral hypoglycemic medication); (3) serum triglycerides 150 mg/dl; (4) HDLcholesterol <40 mg/dl in men or <50 mg/dl in women; (5) waist circumference >90 cm in men or >85 cm in women. Cutoff value for waist circumference was modified according to that recommended by the Korean Society for Study of Obesity. Definition of metabolic syndrome in adolescents (10–19 years old) was according to age-modified standards of NCEP ATP-III criteria [18]. Adolescents who presented 3 or more criteria were considered to have metabolic syndrome: (1) elevated blood pressure (systolic or diastolic blood pressure) greater than or equal to the age and sex-specified 90th percentile except for those aged 18 and 19, defined as 130/ 85 mm Hg; (2) fasting glucose 100 mg/dl (recommended by International Diabetes Federation, 2004); (3) serum triglycerides 110 mg/dl; (4) HDL-cholesterol <40 mg/dl; (5) waist circumference the age and sex-specific 90th percentile for this population.

Age-adjusted

Definition of metabolic syndrome

Husband

2.3.

diabetes research and clinical practice 93 (2011) 430–436

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diabetes research and clinical practice 93 (2011) 430–436

Table 3 – Odds ratio for having metabolic syndrome by marriage duration. Marriage duration (year)

Wife

Husband No. of people

No. of people with metabolic syndrome

439 393 347 403

32 52 98 190

1–10 11–20 21–30 31

Odds ratioa (95% CI) 1.01 0.95 1.58 1.53

(0.43–2.38) (0.51–1.76) (0.96–2.61) (1.01–2.30)

No. of people

No. of people with metabolic syndrome

443 397 339 399

105 139 129 166

Odds ratioa (95% CI) 1.06 0.94 1.58 1.53

(0.45–2.49) (0.51–1.75) (0.96–2.61) (1.01–2.30)

Marriage duration = wife’s age her first childbirth age. Adjusted for age, spousal age odds ratio of having metabolic syndrome or its component in one spouse whose partner had same abnormality, compared with those whose partner did not. a

did not have metabolic syndrome. Similarly, the multivariateadjusted odds ratio for metabolic syndrome for wives whose husbands had metabolic syndrome was 1.41 (95% CI: 1.08–1.84) compared to those whose husbands did not have syndrome. When we defined central obesity for women as waist circumference >80 cm instead of >85 cm, the strength of association was slightly decreased however the difference was not significant (Supplement Table 1). Couples married for over 20 years showed an increased concordant odds ratio, however couples married less than 20 years did not show a statistically significant concordance (Table 3). Table 4 shows the concordant odds ratio for metabolic syndrome between parents and their offspring. Compared with children whose parents did not have metabolic syndrome, the multivariate-adjusted odds ratio in children with at least one parent with metabolic syndrome was 2.56 (95% CI: 1.09–5.98). The multivariate-adjusted odds ratio in offspring whose father had the metabolic syndrome was 4.22 (95% CI: 1.11–16.10), and for offspring whose mother had metabolic syndrome, the multivariate-adjusted odds ratio was 6.48 (95% CI: 1.53–27.39). Lastly, in families where both parents have metabolic syndrome, the multivariate-adjusted odds ratio was 15.58 (95% CI: 3.11–78.17) (Table 5). The age-adjusted intraclass correlation coefficients of metabolic variables and dietary factors in the six combinations of family members ranged from 0.02 to 0.52 (Supplement Table 3). The highest correlation among family members was observed in father–daughter combinations for levels of triglycerides (0.52), while the correlation for metabolic factors in the husband–wife combination was the lowest (waist circumference, fasting blood glucose, HDL-cholesterol, triglyceride; 0.02). In general, higher correlation coefficients were observed among siblings compared to other parent–offspring combinations, and both were much higher than spouses. The intraclass correlation coefficients of dietary factors between spouses were higher than any other variables (total energy intake, 0.33; protein consumption, 0.38; fat consumption, 0.43).

4.

Discussion

In this nationally representative study, we observed strong familial concordance for metabolic syndrome and each of its components. Individuals whose marital partner had metabolic syndrome had an increased risk of having metabolic syndrome when compared with those whose partner did

not have metabolic syndrome. The risk of the metabolic syndrome in offspring was also highly correlated with parental status of metabolic syndrome. Previous studies on metabolic syndrome showed significant familial correlation in the parent–children relationship [19–21]. The positive spousal concordance of metabolic syndrome has been reported [22]. In this study we looked at the entire familial concordance with metabolic syndrome. The increased risk of the same metabolic abnormality within genetically unrelated spouse supports the idea that shared environmental factors contributes to the development of the metabolic syndrome. In a couple with longer duration of marriage, there was a higher concordant odds ratio of metabolic syndrome. Couples tended to be exposed to more common environment factors the longer they had been married. The stronger association in parent– offspring combinations also shows that the genetic background further enhances the association, indicating that the mechanism is likely to be predominantly mediated by the environmental challenge on the permissive genetic background [14]. Compared with most Western populations, Koreans have a lower waist circumference for the same level of cardiovascular risk [23,24]. Several studies have thus used various modified criteria for abdominal obesity in Asian populations [17,25]. Our study used the criteria recommended in the report by the Korean Society for the Study of Obesity. Some previous studies suggested that socioeconomic status can contribute to metabolic syndrome [26,27]. However, when we additionally adjusted for education level, there was no significant change in odds ratios (Supplement Table 2). Parents provide both the genes and environment for their offspring. There was a stronger association between maternal–offspring combinations than paternal combinations for metabolic syndrome. This might be due to the traditional role of mothers who are more involved in the upbringing of their offspring including meal preparation, and home education. In contrast, fathers are traditionally responsible for providing the financial support for the family. However, when both parents tested positive for metabolic syndrome, offspring showed a large increase in risk of having the metabolic syndrome. Among metabolic syndrome components, the abdominal obesity was a significant increased risk in offspring whose parents have metabolic syndrome. Parental obesity has been identified as a predominant risk factor, with childhood fatness increasing linearly with an increasing level of parental fatness [28,29]. Likewise, evidence suggests that those individuals we are in close contact form the reference for normal weight

434

(1.06–3.68) (0.86–2.56) (0.98–2.03) (0.72–2.30) (0.39–2.93)

c

b

a

Odds ratio of having metabolic syndrome or its component in one parent whose offspring had same abnormality, compared with those whose offspring did not. Odds ratio of having metabolic syndrome or its component in one offspring whose parent had same abnormality, compared with those whose parent did not. Adjusted for age, smoking status, alcohol consumption, exercise status, stress intensity.

1.98 1.48 1.41 1.30 1.07 (1.28–2.56) (1.30–2.27) (1.03–1.67) (0.88–1.61) (0.59–2.00) 1.81 1.72 1.33 1.19 1.09 75 124 278 275 21 148 259 682 786 46 (1.22–2.16) (1.12–1.95) (1.32–2.26) (1.01–1.79) (1.10–1.99) 1.62 1.48 1.73 1.35 1.48 (1.20–2.12) (1.19–2.03) (1.54–2.00) (1.47–1.93) (1.11–1.98) 96 118 212 116 88 381 466 866 533 372

1.60 1.56 1.54 1.47 1.48

2.56 (1.09–5.98) 2.01 (1.48–2.73) 207

102

Multivariate-adjustedc Age-adjusted Multivariate-adjustedc

1.90 (1.43–2.53) 1.87 (1.42–2.48) 107 409

Metabolic syndrome Each metabolic risk factor Waist circumference Triglyceride HDL-cholesterol Blood pressure Fasting blood glucose

No. of people

No. of people with offspring had same abnormality

Age-adjusted

Odds ratio (95% CI)

a

Parent

Table 4 – Odds ratio(95% CI) of metabolic syndrome and its each metabolic risk factor.

No. of people

No. of people with parents had same abnormality

Offspring

Odds ratiob (95% CI)

diabetes research and clinical practice 93 (2011) 430–436

status [30,31]. In those families where the norm is obese, it is likely that increases in the weight of the offspring go unnoticed relative to those whose parents have normal weight levels. It is important to evaluate familial factors to identify strategies for the prevention and management of the metabolic syndrome. Genetic studies of metabolic syndrome components and insulin resistance traits also can contribute to the condition, including the observed familial clustering of these parameters [32,33]. For instance, the Framingham family data indicated a twofold increase in the sibling recurrence risk ratios for the metabolic syndrome [32]. The development of metabolic syndrome exhibits a complex interaction among genes and environmental factors. Most studies have only investigated the individual metabolic syndrome components and did not consider it as a whole [34]. Due to the clustering of multiple risk factors, metabolic syndrome is a difficult phenotype to dissect due to underlying genetic factors [21]. Several twin studies have demonstrated that both genetic and shared environmental factors play important roles in the etiology of metabolic syndrome [35,36]. The present study showed a high correlation among family members for metabolic variables and intake of dietary components. Familial resemblance in nutrient intake has been reported between spouses [37,38] and between parents and their children [39]. In this study, the correlation for dietary factors was high among family members, especially between spouses. All metabolic variables were highly correlated between siblings. Most of the intraclass correlations obtained from our study are similar to those reported in other literature [19–21]. One of the important strengths of our study is that we investigated the familial concordance of the metabolic syndrome in a large representative sample. External validity is therefore sufficient to extrapolate the findings to the general Korean population. In addition, we analyzed lifestyle factors in this study, including physical activity, smoking status, alcohol drinking, and nutrient intake that might influence components of the metabolic syndrome. Only few familial aggregation studies have investigated lifestyle factors involved in the etiology of the metabolic syndrome. A limitation of this study was that not all offspring in this study were likely to be the biological offspring of their parents and those that had been adopted would not have shared the same familial environment or genetic background. However, this misclassification would have attenuated the observations towards the null, thus supporting our conclusions regarding the contribution of both environmental and genetic parameters to the familial concordance of the metabolic syndrome. Secondly, this study was a cross-sectional study, suggesting that caution should be used in causal interpretation of these findings. However, given the temporality in the spousal concordance whereby only those married for over 20 years exhibited a significant association, again supports these observations. In conclusion, our study revealed that there is significant familial concordance for metabolic syndrome and its components in Korean families. The findings imply targeting early screening or disease prevention measures at family members with metabolic syndrome may be a useful approach to

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Table 5 – Odds ratio(95% CI) for metabolic syndrome in offspring by parental metabolic syndrome. Status of metabolic syndrome in parents

No. of people

No. of people with metabolic syndrome

831 336 238 63

105 71 47 16

None Father Mother Both parents

Odds ratioa (95% CI) Age-adjusted

Multivariate-adjustedb

1.00 2.01 (1.43–2.82) 2.35 (1.57–3.52) 3.31 (1.75–6.28)

1.00 4.22 (1.11–16.10) 6.48 (1.53–27.39) 15.58 (3.11–78.17)

a Odds ratio of having metabolic syndrome or its component in one parent whose offspring had same abnormality, compared with those whose offspring did not. b Adjusted for age, parents age, smoking status, alcohol consumption, exercise status, stress intensity.

attenuate the increased morbidity and mortality in those with the condition.

[8]

Acknowledgements This work was supported by a grant of the Korea Healthcare Technology R&D Project, Ministry of Health and Welfare, Republic of Korea (A102065).

Appendix ASupplementary data Supplementary data associated with this article can be found, in the online version, at doi:10.1016/j.diabres.2011.06.002.

Conflict of interest

[9]

[10]

[11]

[12]

There are no conflicts of interest.

references

[1] Klein BE, Klein R, Lee KE. Components of the metabolic syndrome and risk of cardiovascular disease and diabetes in Beaver Dam. Diabetes Care 2002;25(10):1790–4. [2] Thomas GN, Schooling CM, McGhee SM, Ho SY, Cheung BM, Wat NM, et al. Metabolic syndrome increases all-cause and vascular mortality: the Hong Kong Cardiovascular Risk Factor Study. Clin Endocrinol (Oxf) 2007;66(5):666–71. [3] Isomaa B, Almgren P, Tuomi T, Forsen B, Lahti K, Nissen M, et al. Cardiovascular morbidity and mortality associated with the metabolic syndrome. Diabetes Care 2001; 24(4):683–9. [4] Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J, et al. The metabolic syndrome and total and cardiovascular disease mortality in middleaged men. JAMA 2002;288(21):2709–16. [5] Knuiman MW, Divitini ML, Welborn TA, Bartholomew HC. Familial correlations, cohabitation effects, and heritability for cardiovascular risk factors. Ann Epidemiol 1996;6(3):188–94. [6] Jee SH, Suh I, Won SY, Kim MY. Familial correlation and heritability for cardiovascular risk factors. Yonsei Med J 2002;43(2):160–4. [7] Hunt KJ, Heiss G, Sholinsky PD, Province MA. Familial history of metabolic disorders and the multiple metabolic

[13]

[14]

[15]

[16]

[17]

[18]

syndrome: the NHLBI family heart study. Genet Epidemiol 2000;19(4):395–409. Alberti KG, Zimmet PZ. Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1. Diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998;15(7):539–53. Mo-suwan L, Tongkumchum P, Puetpaiboon A. Determinants of overweight tracking from childhood to adolescence: a 5 y follow-up study of Hat Yai schoolchildren. Int J Obes Relat Metab Disord 2000;24(12):1642–7. Perusse L, Tremblay A, Leblanc C, Cloninger CR, Reich T, Rice J, et al. Familial resemblance in energy intake: contribution of genetic and environmental factors. Am J Clin Nutr 1988;47(4):629–35. Carmelli D, Cardon LR, Fabsitz R. Clustering of hypertension, diabetes, and obesity in adult male twins: same genes or same environments? Am J Hum Genet 1994;55(3):566–73. Perusse L, Rice T, Despres JP, Bergeron J, Province MA, Gagnon J, et al. Familial resemblance of plasma lipids, lipoproteins and postheparin lipoprotein and hepatic lipases in the HERITAGE Family Study. Arterioscler Thromb Vasc Biol 1997;17(11):3263–9. Knuiman MW, Divitini ML, Bartholomew HC. Spouse selection and environmental effects on spouse correlation in lung function measures. Ann Epidemiol 2005;15(1): 39–43. Hippisley-Cox J, Coupland C, Pringle M, Crown N, Hammersley V. Married couples’ risk of same disease: cross sectional study. BMJ 2002;325(7365):636. Choi YJ, Kim HC, Kim HM, Park SW, Kim J, Kim DJ. Prevalence and management of diabetes in Korean adults: Korea National Health and Nutrition Examination Surveys 1998–2005. Diabetes Care 2009;32(11):2016–20. Friedewald WT, Levy RI, Fredrickson DS. Estimation of the concentration of low-density lipoprotein cholesterol in plasma, without use of the preparative ultracentrifuge. Clin Chem 1972;18(6):499–502. Alberti KG, Eckel RH, Grundy SM, Zimmet PZ, Cleeman JI, Donato KA, et al. Harmonizing the metabolic syndrome: a joint interim statement of the International Diabetes Federation Task Force on Epidemiology and Prevention; National Heart, Lung, and Blood Institute; American Heart Association; World Heart Federation; International Atherosclerosis Society; and International Association for the Study of Obesity. Circulation 2009;120(16):1640–5. Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (Adult Treatment Panel III). JAMA 2001;285(19):2486–97.

436

diabetes research and clinical practice 93 (2011) 430–436

[19] Azizi F, Farahani ZK, Ghanbarian A, Sheikholeslami F, Mirmiran P, Momenan AA, et al. Familial aggregation of the metabolic syndrome: Tehran Lipid and Glucose Study. Ann Nutr Metab 2009;54(3):189–96. [20] Park HS, Park JY, Cho SI. Familial aggregation of the metabolic syndrome in Korean families with adolescents. Atherosclerosis 2006;186(1):215–21. [21] Chien KL, Hsu HC, Chen WJ, Chen MF, Su TC, Lee YT. Familial aggregation of metabolic syndrome among the Chinese: report from the Chin-Shan community family study. Diabetes Res Clin Pract 2007;76(3):418–24. [22] Kim HC, Kang DR, Choi KS, Nam CM, Thomas GN, Suh I. Spousal concordance of metabolic syndrome in 3141 Korean couples: a nationwide survey. Ann Epidemiol 2006;16(4):292–8. [23] Kim MH, Kim MK, Choi BY, Shin YJ. Prevalence of the metabolic syndrome and its association with cardiovascular diseases in Korea. J Korean Med Sci 2004;19(2):195–201. [24] Oh JY, Hong YS, Sung YA, Barrett-Connor E. Prevalence and factor analysis of metabolic syndrome in an urban Korean population. Diabetes Care 2004;27(8):2027–32. [25] Tan CE, Ma S, Wai D, Chew SK, Tai ES. Can we apply the National Cholesterol Education Program Adult Treatment Panel definition of the metabolic syndrome to Asians? Diabetes Care 2004;27(5):1182–6. [26] Lee WY, Jung CH, Park JS, Rhee EJ, Kim SW. Effects of smoking, alcohol, exercise, education, and family history on the metabolic syndrome as defined by the ATP III. Diabetes Res Clin Pract 2005;67(1):70–7. [27] Park MJ, Yun KE, Lee GE, Cho HJ, Park HS. A cross-sectional study of socioeconomic status and the metabolic syndrome in Korean adults. Ann Epidemiol 2007; 17(4):320–6. [28] Guillaume M, Lapidus L, Beckers F, Lambert A, Bjorntorp P. Familial trends of obesity through three generations: the Belgian-Luxembourg child study. Int J Obes Relat Metab Disord 1995;19(Suppl. 3):S5–9.

[29] Rice T, Perusse L, Bouchard C, Rao DC. Familial aggregation of body mass index and subcutaneous fat measures in the longitudinal Quebec family study. Genet Epidemiol 1999;16(3):316–34. [30] Eckstein KC, Mikhail LM, Ariza AJ, Thomson JS, Millard SC, Binns HJ. Parents’ perceptions of their child’s weight and health. Pediatrics 2006;117(3):681–90. [31] Huang JS, Becerra K, Oda T, Walker E, Xu R, Donohue M, et al. Parental ability to discriminate the weight status of children: results of a survey. Pediatrics 2007;120(1):e112–9. [32] Chen WJ, Liu PH, Ho YY, Chien KL, Lo MT, Shih WL, et al. Sibling recurrence risk ratio analysis of the metabolic syndrome and its components over time. BMC Genet 2003;4(Suppl. 1):S33. [33] Xiang AH, Azen SP, Raffel LJ, Tan S, Cheng LS, Diaz J, et al. Evidence for joint genetic control of insulin sensitivity and systolic blood pressure in hispanic families with a hypertensive proband. Circulation 2001;103(1):78–83. [34] Flodmark CE, Ohlsson T, Ryden O, Sveger T. Prevention of progression to severe obesity in a group of obese schoolchildren treated with family therapy. Pediatrics 1993;91(5):880–4. [35] Poulsen P, Vaag A, Kyvik K, Beck-Nielsen H. Genetic versus environmental aetiology of the metabolic syndrome among male and female twins. Diabetologia 2001;44(5):537–43. [36] Mayer EJ, Newman B, Austin MA, Zhang D, Quesenberry Jr CP, Edwards K, et al. Genetic and environmental influences on insulin levels and the insulin resistance syndrome: an analysis of women twins. Am J Epidemiol 1996;143(4): 323–32. [37] Eastwood MA, Brydon WG, Smith DM, Smith JH. A study of diet, serum lipids, and fecal constituents in spouses. Am J Clin Nutr 1982;36(2):290–3. [38] Lee J, Kolonel LN. Nutrient intakes of husbands and wives: implications for epidemiologic research. Am J Epidemiol 1982;115(4):515–25. [39] Mirmiran P, Mirbolooki M, Azizi F. Familial clustering of obesity and the role of nutrition: Tehran Lipid and Glucose Study. Int J Obes Relat Metab Disord 2002;26(12):1617–22.