Abstract
Obesity is a disease threatening health and is known one of risk factors causing chronic disease. In Traditional Korean Medicine, bi-sup is casus of obesity. Wnt10b has been indicated as a potential regulator of adipogenesis in vivo and in vitro models of obesity. To analyze the distribution of wnt10b polymorphism between bi-sup group and non-bi-sup group in Korean elder subjects with cerebral infarction (CI). The study group was composed of patients with CIwho were admitted to one of the thirteen Korean oriental medical hospitals participating in this study from 2009 to 2010. A total of 670 CI patients, including 416 with bi-sup group and 254 with non-bi-sup group, participated in this study. Genotype of G-607C was conducted by primer extension using TaqManprobe and five percent of subjects were re-genotyped by direct sequencing to confirm the accuracy of the genotyping. The association of the SNP with the bi-sup group versus non-bi-sup group was performed by multiple logistic regression. Frequency of C allele in bi-sup was 45.75% which was significantly lower than 56.69% in non-bi-sup (P=0.0043, OR=0.628 [0.453-0.864]). Subjects with GC or CC type in bi-sup was also 72.36%, that was also small compared with 78.35% in non-bi-sup (P=0.0467, OR=0.675 [0.458-0.994]). These results suggest that G-607C might be used as a diagnostic genetic marker for bi-sup in stroke patients and in the development of personalized medical care.
Keywords: wnt10b, G-607C, bi-sup, obesity, stroke, cerebral infarction, traditional Korean medicine
Introduction
Obesity is a disease threatening health and is known one of risk factors causing chronic disease such as hypertension, diabetes and atherosclerosis. Obese patients were currently increasing in worldwide. In Korea, adults subjects with obesity (BMI≥25 kg/m2) is about 30.8% in over 19 aged people at 2010, which was higher than 26% at 1998. Especially, ratio of obesity in man was 36.3%, which is significantly high compared with woman. Because increase of obese subject related with social problems such as raise of disease incidence, decrease of social activity, many efforts is being done to decrease obese population in the world [1].
Particularly, obesity is a risk factor for the onset of stroke [2]. Risk ratio of stroke in man is increasing about 11% whenever BMI increase 1 kg/m2 [3], and relationship between increase of BMI and onset of ischemic stroke was well known in woman [4]. In Donguibogam written at 1610, the relationship between obesity and stroke was described that stroke more occurs in obese subjects than non-obese subjects. It means that obesity is recognized as a risk factor for stroke from old period [5].
In Traditional Korean Medicine (TKM), obese man is defined that shoulder is wide, neck and skin are thick, color of skin is dark, and mouth and lip are large. That also say that bi-sup is casus of obese man [6]. The bi-sup is a symptom of accumulation of excess waste in body caused by preventing circulation of fat, body fluid, Qi and blood, and that occur by erudite and overeating of alcohol, meat and fatty food. That is similar with cause of obesity saying in western medicine [5].
Bi-sup of subjects is determined by oriental medical doctor through combination of body, Visage skin thickness etc., and that was some different obese index such as BMI used in western medicine. Previous studies used BMI or waist circumference as indicator of diagnosis of pattern identification instead of bi-sup, but the results some different compared with bi-sup as index of pattern identification [7-9]. Recently study showed that bi-sup was correlated with BMI but not with waist and WHR, an index of abdominal obesity [6], that means western medicine has a limitation to explain the concept of bi-sup used in oriental medicine.
Wingless-type MMTV integration sites (WNTs) is signal molecules binding Frizzled receptor and low-density lipoprotein receptor-related protein 5/6 co-receptor and regulate several physical activity [10]. Especially, wnt10b is known to inhibit adipogenesis throughout β-catenin pathway [11]. Recent reports showed that singe nucleotide polymorphism in wnt10b promoter region affect obesity, fat mass and abdominal obesity in various population [12-14].
In this study, we elucidated bi-sup from ischemic stroke and association between bi-sup and SNP of wnt10b gene.
Subjects and methods
Subjects
This study was performed as parts of the project “The Fundamental Study for the Standardization and Objectification of Pattern Identification in TKM for Stroke (SOPI-Stroke)” in the Korean Institute of Oriental Medicine (KIOM) [15]. Patient with ischemic cerebral infarction (CI) and within one month after onset were enrolled from 2009 to 2010 in to 13 Korean oriental medical hospitals. All subjects were confirmed CI by magnetic resonance imaging (MRI) and computerized tomography (CT). Subjects with transient ischemic attack or external hemorrhage were excluded in this study. Subjects to be not recorded questionnaire by confusion of consciousness or difficult communication were also excluded. Clinical information was collected using questionnaire based on ‘Standard of TKM stroke PI’ [16]. This study was performed after receiving informed content from each subjects and approval of the Institutional Review Boards of the KIOM.
Diagnosis of bi-sup and patter identification
Determining bi-sup from each of subject was diagnosed by two experts of TKM within 24 hours and subjects, which were differently diagnosed by two experts, were excluded in study. 416 subjects with bi-sup and 254 without bi-sup were enrolled and general characteristics of those were presented in Table 1.
Table 1.
Demographic parameters of study subjects
| Characteristics | Non-Bi-Sup | Bi-Sup | P |
|---|---|---|---|
| N | 254 | 416 | |
| Sex (M/F) | 119/135 | 150/266 | 0.0057 |
| Age (year) | 73 (65, 79) | 69 (60, 76) | <0.0001* |
| Smoking (none/stop/active) | 122/51/81 | 259/77/80 | 0.0003 |
| Drinking (none/stop/active) | 128/38/88 | 230/50/136 | 0.3817 |
| TOAST classification | |||
| LAA | 79 | 119 | 0.6249 |
| CE | 21 | 30 | |
| SVO | 135 | 245 | |
| SOE | 7 | 8 | |
| SUE | 11 | 13 | |
| Medical history | |||
| TIA (Yes, %) | 38 (15.08) | 49 (11.86) | 0.2330 |
| Hypertension (Yes, %) | 140 (55.12) | 269 (64.66) | 0.014 |
| Hyperlipidemia (Yes, %) | 26 (10.32) | 65 (15.7) | 0.0498 |
| Diabetes (Yes, %) | 56 (22.05) | 117 (28.26) | 0.0751 |
| Heart disease (Yes, %) | 16 (6.3) | 28 (6.76) | 0.8144 |
All results except age are expressed as frequencies for categorical variables. Age is expressed as Median (25% percentile, 75% percentile). ICH: intracerebral hemorrhage. CI: cerebral infarction. TOAST: Trial of ORG 10172 in Acute Stroke Treatment. LAA: large-artery atherosclerosis. CE: cardioembolism. SVO: small-vessel occlusion. SOE: stroke of other etiology. SUE: stroke of undetermined etiology. TIA: transient ischemic attack. P-value of Bi-Sup versus Non-Bi-Sup using a Student’s t-test or Wilcoxon rank-sum test (*) in continuous variables, chi-square test in categorical variables. P-values with statistical significance were presented in bold (<0.05).
Genotyping of G-607C SNP
After agreement of subject, blood were collected and genomic DNA were precipitated using a GeneAll Genomic Isolation Kit (GeneAll, Seoul, Korea) according to manufactual protocol. Genotype of G-607C SNP in wnt10b was determined by PCR and Taqman probe. The primerset was purchased from Applied Biosystems Inc. (USA), and genotyping were analyzed by Macrogen (Seoul, Korea). To confirm the accuracy of the genotype analysis, five percent of subjects were randomly selected, and the SNP were genotyped by the sequencing method using the ABI 3700 sequencer (ABI Inc. Carlsbad, CA USA). The genotyping agreement between two method was 98% and a kappa value was 0.98 (CI 0.92-1.0), which represents high accuracy. The concordance of G-607C withHardy-Weinberg equilibrium (HWE) was tested using HapAnalyzer software (http://www.genenames.org) [17].
Statistical analysis
All statistical analysis was performed using SAS software version 9.1.3 (SAS Institute Inc., NC). The χ2-test was used to compare the differences between categorical variables. Student t-test or Kruskal-Wallis rank-sum test was used to compare the difference of continuous variables after normality test by a Kolmogorov-Smirnov test. Association of G-607C with bi-sup was examined by binary logistic regression model adjusted with sex and age or sex, age, smoking, drinking and hypertension. Effects of G-607C on body characteristics or serum parameter were evaluated using general linear model after adjustment for sex, age, smoking hypertension, hyperlipidemia and diabetes. Statistical significance was determined as P<0.05.
Results
Difference of body characteristics and serum parameters between bi-sup and non-bi-sup was shown in Table 2. Each of BMI and waist circumference in bi-sup was 25.83 kg/m2 and 92.28 cm, which were significantly higher than non-bi-sup (P<0.0001). Body weight also increased in bi-sup. Among serum parameters, level of triglyceride was significantly high in bi-sup compared with non-bi-sup (P=0.0039), but GOP and HDL-cholesterol was lower in bi-sup than non-bi-sup (P=0.0199 and P=0.0181, respectively).
Table 2.
Differences in body characteristics and serum parameters among the study subjects
| Characteristics | Non-Bi-Sup | Bi-Sup | P |
|---|---|---|---|
| N | 254 | 416 | |
| Body characteristics | |||
| Weight (kg) | 54.99±9.69 | 65.83±9.82 | <.0001 |
| BMI (kg/m2) | 21.33±2.48 | 25.83±2.81 | <.0001 |
| Waist circumference (cm) | 81.24±8.26 | 92.28±7.45 | <.0001 |
| WHR | 0.95±0.18 | 0.95±0.08 | 0.2703 |
| Serum parameter | |||
| GOP (U/ml) | 28.45±18.36 | 25.88±11.94 | 0.0199 |
| GPT (U/ml) | 25.3±22.26 | 24.5±15.89 | 0.1962 |
| Total cholesterol (mg/dL) | 183.0±44.71 | 189.5±49.31 | 0.0915 |
| Triglyceride (mg/dL) | 139.5±112.1 | 171.0±122.3 | 0.0039 |
| HDL-cholesterol (mg/dL) | 44.78±13.11 | 42.57±10.20 | 0.0181 |
| Blood sugar (mg/dL) | 110.2±48.61 | 115.7±38.56 | 0.4012 |
All of the results are expressed as the mean ± standard deviation. BMI: body mass index. WHR: waist-hip ratio. GOP: glutamate oxaloacetate transaminase. GPT: glutamate pyruvate transaminase. P-values were adjusted for sex, age, smoking, hypertension and hyperlipidemiausing a general linear model. P-values with statistical significance are presented in bold (<0.05).
Table 3 showed allele and genotype distribution of G-607C of wnt10b gene between bi-sup and non bi-sup. After adjustment for sex, age or sex, age, smoking, drinking, hypertension and hyperlipidemia, frequency of C allele in bi-sup was 47.84% which was significantly lower than 53.35% in non-bi-sup (P=0.0348, OR=0.779 [0.618-0.982]). Subjects with GC or CC type in bi-sup was also 72.36%, that was also small compared with 78.35% in non-bi-sup (P=0.0467, OR=0.675 [0.458-0.994]).
Table 3.
Genotype distribution of the G-607C polymorphism in the Bi-Sup and Non-Bi-Sup group
| Model | Genotype | Non-Bi-Sup | Bi-Sup | †OR [95% CI] | P | ‡OR [95% CI] | P |
|---|---|---|---|---|---|---|---|
| Allele | G | 237 (46.65) | 434 (52.16) | 0.792 [0.632, 0.991] | 0.0419 | 0.779 [0.618, 0.982] | 0.0348 |
| C | 271 (53.35) | 398 (47.84) | |||||
| §Do | GG | 55 (21.65) | 115 (27.64) | 0.703 [0.484, 1.022] | 0.0649 | 0.675 [0.458, 0.994] | 0.0467 |
| GC+CC | 199 (78.35) | 301 (72.36) | |||||
| §R | GG+GC | 182 (71.65) | 319 (76.68) | 0.764 [0.531, 1.097] | 0.1446 | 0.761 [0.524, 1.104] | 0.1506 |
| CC | 72 (28.35) | 97 (23.32) |
Data are presented as frequencies (percentages).
ORs after adjustment for sex and age, hypertension and hyperlipidemia.
ORs after adjustment for sex, age, smoking, hypertension and hyperlipidemia.
P-values were calculated by logistic regression analysis.
Do and R denote dominant and recessive models, respectively.
P-values with statistical significance are presented in bold (<0.05).
Difference of body characteristics and serum parameters among genotypes of G-607C was shown in Table 4. Among serum parameters, GOP was increased in subjects with C allele compared with subjects with GG type in dominant model (P=0.0463). BMI and waist circumference in body characteristics and total cholesterol and triglyceride in serum parameters were slightly decreased in subjects with CC type compared with subjects with GG or GC type without significant difference. Previous report showed that G-607C was related with obese phenotypes in Korean female [12], and we also obtained similar results, but not significant (Tables 5, 6).
Table 4.
Association of the G-607C polymorphism with clinical parameters among the study subjects
| Variable | Genotype | P | ||||
|---|---|---|---|---|---|---|
|
| ||||||
| GG (n=170) | GC (n=331) | CC (n=169) | §Co | §Do | §R | |
| Body characteristics | ||||||
| Weight (kg) | 61.38±11.83 | 62.63±10.99 | 60.6±10.42 | 0.1559 | 0.4864 | 0.1450 |
| BMI (kg/m2) | 24.21±3.42 | 24.28±3.62 | 23.87±3.16 | 0.6149 | 0.8047 | 0.3258 |
| Waist circumference (cm) | 88.07±9.84 | 88.44±9.62 | 87.13±8.65 | 0.4538 | 0.9648 | 0.2392 |
| WHR | 0.95±0.1 | 0.95±0.12 | 0.95±0.18 | 0.9974 | 0.9870 | 0.9428 |
| Serum parameter | ||||||
| GOP (U/ml) | 24.81±10.57 | 27.36±15.78 | 27.94±16.09 | 0.1246 | 0.0463 | 0.2748 |
| GPT (U/ml) | 23.48±16.54 | 25.38±19.7 | 24.99±18.14 | 0.5177 | 0.2519 | 0.7489 |
| Total cholesterol (mg/dL) | 190.53±51.43 | 188.4±47.76 | 181.18±43.15 | 0.1482 | 0.2402 | 0.0618 |
| Triglyceride (mg/dL) | 168.84±147.33 | 160.04±115.06 | 147.9±94.2 | 0.3611 | 0.2553 | 0.2306 |
| HDL-cholesterol (mg/dL) | 43.92±11.08 | 43.56±11.29 | 42.41±11.81 | 0.5200 | 0.3824 | 0.3224 |
| Blood sugar (mg/dL) | 109.14±29.03 | 117.11±50.51 | 111.6±35.64 | 0.1410 | 0.1159 | 0.5475 |
Data are presented as the mean ± S.D. P-values, representing adjusted odds ratios, were adjusted for sex, age, smoking, hypertension and hyperlipidemia using a general linear model.
Co, Do and R denote codominant, dominant and recessive models, respectively.
P-values with statistical significance are presented in bold (<0.05).
Table 5.
Demographic parameters of the obese group and non-obese group according to the BMI
| Characteristics | Non-Obese | Obese$ | P |
|---|---|---|---|
| N | 361 | 239 | |
| Sex (M/F) | 146/215 | 97/142 | 0.9722 |
| Age (year) | 69.92±10.66 | 66.23±10.29 | <0.0001 |
| Smoking (none/stop/active) | 187/75/99 | 154/38/47 | 0.0090 |
| Drinking (none/stop/active) | 181/51/129 | 137/27/75 | 0.2135 |
| TOAST classification | |||
| LAA | 103 | 68 | 0.9978 |
| CE | 25 | 16 | |
| SVO | 210 | 141 | |
| SOE | 9 | 5 | |
| SUE | 13 | 9 | |
| Medical history | |||
| TIA (Yes, %) | 47 (13.06) | 31 (12.97) | 0.9759 |
| Hypertension (Yes, %) | 200 (55.40) | 159 (66.53) | 0.0065 |
| Hyperlipidemia (Yes, %) | 44 (12.26) | 42 (17.57) | 0.0695 |
| Diabetes (Yes, %) | 87 (24.10) | 68 (28.57) | 0.2214 |
| Heart disease (Yes, %) | 27 (7.48) | 14 (5.86) | 0.4409 |
All of the results, except age, are expressed as frequencies for the categorical variables.
Obese: BMI≥25.
ICH: intracerebral hemorrhage. CI: cerebral infarction. TOAST: Trial of ORG 10172 in Acute Stroke Treatment. LAA: large-artery atherosclerosis. CE: cardioembolism. SVO: small-vessel occlusion. SOE: stroke of other etiology. SUE: stroke of undetermined etiology. TIA: transient ischemic attack. P-values of Obesegroup versus Non-Obesegroupusing Student’s t-test for continuous variables and the chi-square test for categorical variables. P-values with statistical significance are presented in bold (<0.05).
Table 6.
Genotype distribution of the G-607C polymorphism in the obese group and non-obese group according to the BMI
| Model | Genotype | Non-Obese | Obese$ | †OR [95% CI] | P | ‡OR [95% CI] | P |
|---|---|---|---|---|---|---|---|
| Allele | G | 344 (47.65) | 250 (52.30) | 0.827 [0.654, 1.045] | 0.1121 | 0.821 [0.645, 1.044] | 0.1076 |
| C | 378 (52.35) | 228 (47.70) | |||||
| §Do | GG | 85 (23.55) | 63 (26.36) | 0.842 [0.574, 1.235] | 0.3794 | 0.828 [0.559, 1.227] | 0.3472 |
| GC+CC | 279 (76.45) | 176 (73.64) | |||||
| §R | GG+GC | 259 (71.75) | 187 (78.24) | 0.713 [0.483, 1.052] | 0.0881 | 0.710 [0.477, 1.058] | 0.0921 |
| CC | 102 (28.25) | 52 (21.76) |
Data are presented as frequencies (percentages).
Obese: BMI≥25.
ORs afteradjustment for age, smoking and hypertension.
ORsafter adjustment for age, smoking and hypertension.
P-values were calculated by logistic regression analysis.
Do and R denote dominant and recessive models, respectively.
Previous study reported that obesity was related with Dampness-phlegm in stroke population [18]. Because of relationship between bi-sup and obese index, we showed distribution of bi-sup in each of pattern identification (Figure 1). Ratio of subjects with bi-sup was 77.65% in Dampness-phlegm, but 38.67% in Yin-deficiency, 58.64% in Fire/Heat and 50.0% in Qi-deficiency. That means bi-sup is major indicator for diagnosing Dampness-phlegm.
Figure 1.
Distribution of patterns in the subjects in the Bi-Sup and Non-Bi-Sup group. Ratio of subjects with Bi-Sup was 77.65% in Dampness-phlegm, but 38.67% in Yin-deficiency, 58.64% in Fire/Heat and 50.0% in Qi-deficiency. QDP: Qi deficiency pattern. DPP: Dampness-phlegm pattern. BSP: Blood stasis pattern. YDP: Yin deficiency pattern. FHP: Fire-heat pattern.
Discussion
Obesity doesn’t means simple increase of weight, but is serious health problem causing various diseases such as hypertension, disorder of lipid metabolism, diabetes cardiovascular infarction, and stroke [19]. Concept of obese man in TKM is some different with obese subject used in western medicine, in which BMI, waist and WHR is used as standard indices for diagnosing obesity. In TKM, criteria for obesity include body characteristics, features, skin thickness etc. HuangdiNeijing explained that obese man has three types, fat, cream and meat, and difference of large or small of appearance, Qi, blood, cold and heat pattern. Because of these differences, cause of obesity is distinguished [20]. That means different treatment should be used when the pathogen of obesity is different [5].
Bi-sup is caused by environmental factors such as lifestyle, diet, stress etc., otherwise no impacts of inherent factors such as genetic variation is known until now. But some report suggested that genetic variations might affect Bi-sup. Kim et al reported that bi-sup co-related with BMI [6], which is same results obtained in this study (Table 2). In previous study reported by Ko et al, NPY, a neurotransmitter affecting diet absorbs, is associated with Dampness-phlegm [21]. Dampness-phlegm is major pattern identification represented in subjects with bi-sup in this study (Figure 1).
TKM categorizes the procedure for determining the pattern of particular patient is called pattern identification.Previous reports have described the pattern identification process for differentiating stroke victims with four TKM types: the Fire-Heat pattern, Dampness-phlegm pattern, Yin deficiency pattern and Qideficiency pattern [14-16,18].
In this study, we elucidated the association between bi-sup and G-607C SNP in Wnt10b among Korean CI patients enrolled from 2009 to 2010. Frequency of C allele and subjects with GC or CC type were significantly lower in bi-sup than non-bi-sup (Table 3).
Wnt10b, one of Wnt’s family, is secreted signaling protein and regulate several developmental processes through Wnt/β-catenin signaling pathway [11]. Especially, that is known to decrease obesity. Wnt10b inhibited adipogenesis though regulating expression of C/EBP and PPARγ2 [22], and body weight and abdominal fat mass was significantly decreased inob/ob mice with overexpression of wnt10b [23]. Scarda et al also reported that expression of wnt10b in muscle and adipocyte was significantly decreased in obese Zucker rat [24]. Recently, Kim et al showed that C allele of G-607C SNP located at promoter region of wnt10b increased wnt10b expression and this SNP was associated with decrease of abdominal fat mass and BMI in Korean woman [12]. This study also showed similar result that the frequency of CC type was lower in obese subjects than non-obese subjects without significant difference (Table 6).
In TKM, Bi-sup is state accumulating waste in body by preventing circulation of fat, body fluid, Qi and blood, and this is caused by strength of tissue function responding digest, absorption and storage or weakness of tissue function performing breathing, blood circulation, energy expenditure and excretion [25]. The disorder of the tissue functions was occurred by environmental factors such as lifestyle aging, stress etc. And, inherent factor might also affect to that.
In this study, we showed association of G-607C of wnt10b with bi-sup, and more studies will be need to explain a relation between genetic factors and symptoms of disease in a point of view of TKM.
Acknowledgements
This research was supported by a grant from the Korea Institute of Oriental Medicine (K13130, K15111) and Korean Food Research Institute (G15060).
Disclosure of conflict of interest
None.
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